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Special Issue "Sensors for Gait, Posture, and Health Monitoring"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: 30 June 2019

Special Issue Editor

Guest Editor
Prof. Dr. Thurmon Lockhart

School of Biological and Health Systems Engineering, Ira A Fulton Schools of Engineering, Arizona State University, Tempe, AZ, USA
Website | E-Mail
Interests: neuro-rehabilitation; gait and posture; fall risk assessment; nonlinear dynamics; biodynamics; wireless inertial sensors

Special Issue Information

Dear Colleagues,

In recent years, many technologies for gait and posture assessments have emerged. Wearable sensors, active and passive in-house monitors, and many combinations thereof all promise to provide accurate measures of gait and posture parameters. Motivated by market projections for wearable technologies and driven by recent technological innovations in wearable sensors (MEMs, electronic textiles, wireless communications, etc.) the wearable health/performance area is growing rapidly and has the potential to transform the future healthcare from disease treatment to disease prevention.

The objective of this Special Issue is to address and disseminate the latest gait and posture monitoring systems as well as various mathematical models/methods characterizing mobility functions. As such, in this special issue we call on those researchers who have used various sensor technologies and methods to assess gait and postural characteristics among varied populations.

This Special Issue focuses on wearable monitoring systems and physical sensors and its mathematical models that can be utilized in varied environments and varied conditions in monitoring health and performance.

Prof. Dr. Thurmon Lockhart
Guest Editor

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Published Papers (62 papers)

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Open AccessArticle
Electromyographic Evaluation of the Impacts of Different Insoles in the Activity Patterns of the Lower Limb Muscles during Sport Motorcycling: A Cross-Over Trial
Sensors 2019, 19(10), 2249; https://doi.org/10.3390/s19102249
Received: 15 March 2019 / Revised: 23 April 2019 / Accepted: 13 May 2019 / Published: 15 May 2019
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Abstract
Customized foot insoles (CFI) have been recognized to reduce the prevalence of foot disorders in sport. The aim of this study was to evaluate the effect of four types of CFI on the activity patterns of the lower limb muscles (LLM) in healthy [...] Read more.
Customized foot insoles (CFI) have been recognized to reduce the prevalence of foot disorders in sport. The aim of this study was to evaluate the effect of four types of CFI on the activity patterns of the lower limb muscles (LLM) in healthy people during sport motorcycling. Methods: This was a cross-over trial (NCT03734133. Participants were recruited from an outpatient foot specialist clinic. Their mean age was 33 ± 5.14 years. While participants were sport motorcycling in a simulator, the electromyography (EMG) function was registered for LLM via surface electrodes. Participants completed separate tests while wearing one of four types of CFI: (1) only polypropylene (58° Shore D), (2) selective aluminum (60 HB Brinell hardness) in metatarsal and first hallux areas and polypropylene elsewhere (58° Shore D), (3) ethylene vinyl acetate (EVA) (52° Shore A), and (4) standard EVA (25° Shore A) as the control. Results: The activity patterns of the LLM while sport motorcycling showed significantly lower peak amplitude for the selective aluminum CFI than the other types of CFI. Conclusion: EMG amplitude peaks for several LLM were smaller for the hardest CFI (selective aluminum 60 HB Brinell hardness) than the other CFIs (polypropylene 58° Shore D, EVA 52° Shore A, and standard EVA 25° Shore A), except for the fibularis longus in right curves that is increased when the knee touches the road increasing the stability. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
Open AccessArticle
Ultrasonography Features of the Plantar Fascia Complex in Patients with Chronic Non-Insertional Achilles Tendinopathy: A Case-Control Study
Sensors 2019, 19(9), 2052; https://doi.org/10.3390/s19092052
Received: 27 March 2019 / Revised: 25 April 2019 / Accepted: 30 April 2019 / Published: 2 May 2019
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Abstract
Purpose: The goal of the present study was to assess, by ultrasound imaging (USI), the thickness of the plantar fascia (PF) at the insertion of the calcaneus, mid and forefoot fascial locations, and the calcaneal fat pad (CFP) in patients with Achilles tendinopathy [...] Read more.
Purpose: The goal of the present study was to assess, by ultrasound imaging (USI), the thickness of the plantar fascia (PF) at the insertion of the calcaneus, mid and forefoot fascial locations, and the calcaneal fat pad (CFP) in patients with Achilles tendinopathy (AT). Methods: An observational case-control study. A total sample of 143 individuals from 18 to 55 years was evaluated by USI in the study. The sample was divided into two groups: A group composed of the chronic non-insertional AT (n = 71) and B group comprised by healthy subjects (n = 72). The PF thicknesses at insertion on the calcaneus, midfoot, rearfoot and CFP were evaluated by USI. Results: the CFP and PF at the calcaneus thickness showed statistically significant differences (P < 0.01) with a decrease for the tendinopathy group with respect to the control group. For the PF midfoot and forefoot thickness, no significant differences (P > 0.05) were observed between groups. Conclusion: The thickness of the PF at the insertion and the CPF is reduced in patients with AT measured by USI. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Heel Height as an Etiology of Hallux Abductus Valgus Development: An electromagnetic Static and Dynamic First Metatarsophalangeal Joint Study
Sensors 2019, 19(6), 1328; https://doi.org/10.3390/s19061328
Received: 19 December 2018 / Revised: 13 March 2019 / Accepted: 13 March 2019 / Published: 16 March 2019
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Abstract
Background: Hallux abductus valgus (HAV) is a forefoot condition produced by extrinsic and intrinsic factors. Shoes with a high heel height and a typical narrow tip toe box can induce deviations in both the proximal phalanx of the hallux (PPH) and the first [...] Read more.
Background: Hallux abductus valgus (HAV) is a forefoot condition produced by extrinsic and intrinsic factors. Shoes with a high heel height and a typical narrow tip toe box can induce deviations in both the proximal phalanx of the hallux (PPH) and the first metatarsal (IMTT) bones. Nevertheless, the isolated role of heel height remains unclear in the development of HAV pathology. Objectives: The goal was to determine if the heel height increase of shoes without a narrow box toe could augment the PPH and IMTT deviation in frontal, sagittal, and transverse planes toward the first metatarsophalangeal joint (MPJ) and the first metatarsocuneiform joint (MCJ), respectively, during static and dynamic conditions in relation to precursor movements of HAV. Methods: Women with an average age of 25.10 ± 4.67 years were recruited in this cross-sectional study to assess the three planes of motion of PPH and IMTT while wearing high heels with heights at 3, 6, 9 cm and unshod conditions via sandals. The measurements used an electromagnetic goniometer device with sensors placed on medial aspects of the PPH and IMTT bones under static and dynamic conditions. Results: Wearing shoes with a 6 cm heel in dynamic condition may increase the PPH valgus and abduction deviation from 3.15 ± 0.10° to 3.46 ± 0.05° (p < 0.05) and from 1.35 ± 0.28° to 1.69 ± 0.30° (p < 0.001), respectively. In addition, a PPH abduction increase from 1.01 ± 0.36° to 1.31 ± 0.46° (p < 0.05) after wearing shoes with a 6 cm heel height was observed under static conditions. Conclusions: Wearing shoes with a heel height of 6 cm without a narrow box toe interference may produce PPH abduction and valgus deviations related to HAV formation. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Feasibility of Home-Based Automated Assessment of Postural Instability and Lower Limb Impairments in Parkinson’s Disease
Sensors 2019, 19(5), 1129; https://doi.org/10.3390/s19051129
Received: 28 December 2018 / Revised: 1 February 2019 / Accepted: 26 February 2019 / Published: 5 March 2019
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Abstract
A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation [...] Read more.
A self-managed, home-based system for the automated assessment of a selected set of Parkinson’s disease motor symptoms is presented. The system makes use of an optical RGB-Depth device both to implement its gesture-based human computer interface and for the characterization and the evaluation of posture and motor tasks, which are specified according to the Unified Parkinson’s Disease Rating Scale (UPDRS). Posture, lower limb movements and postural instability are characterized by kinematic parameters of the patient movement. During an experimental campaign, the performances of patients affected by Parkinson’s disease were simultaneously scored by neurologists and analyzed by the system. The sets of parameters which best correlated with the UPDRS scores of subjects’ performances were then used to train supervised classifiers for the automated assessment of new instances of the tasks. Results on the system usability and the assessment accuracy, as compared to clinical evaluations, indicate that the system is feasible for an objective and automated assessment of Parkinson’s disease at home, and it could be the basis for the development of neuromonitoring and neurorehabilitation applications in a telemedicine framework. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Effects of Gait Strategy and Speed on Regularity of Locomotion Assessed in Healthy Subjects Using a Multi-Sensor Method
Sensors 2019, 19(3), 513; https://doi.org/10.3390/s19030513
Received: 16 November 2018 / Revised: 14 January 2019 / Accepted: 18 January 2019 / Published: 26 January 2019
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Abstract
The regularity of pseudo-periodic human movements, including locomotion, can be assessed by autocorrelation analysis of measurements using inertial sensors. Though sensors are generally placed on the trunk or pelvis, movement regularity can be assessed at any body location. Pathological factors are expected to [...] Read more.
The regularity of pseudo-periodic human movements, including locomotion, can be assessed by autocorrelation analysis of measurements using inertial sensors. Though sensors are generally placed on the trunk or pelvis, movement regularity can be assessed at any body location. Pathological factors are expected to reduce regularity either globally or on specific anatomical subparts. However, other non-pathological factors, including gait strategy (walking and running) and speed, modulate locomotion regularity, thus potentially confounding the identification of the pathological factor. The present study’s objectives were (1) to define a multi-sensor method based on the autocorrelation analysis of the acceleration module (norm of the acceleration vector) to quantify regularity; (2) to conduct an experimental study on healthy adult subjects to quantify the effect on movement regularity of gait strategy (walking and running at the same velocity), gait speed (four speeds, lower three for walking, upper two for running), and sensor location (on four different body parts). Twenty-five healthy adults participated and four triaxial accelerometers were located on the seventh cervical vertebra (C7), pelvis, wrist, and ankle. The results showed that increasing velocity was associated with increasing regularity only for walking, while no difference in regularity was observed between walking and running. Regularity was generally highest at C7 and ankle, and lowest at the wrist. These data confirm and complement previous literature on regularity assessed on the trunk, and will support future analyses on individuals or groups with specific pathologies affecting locomotor functions. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Weight-Bearing Estimation for Cane Users by Using Onboard Sensors
Sensors 2019, 19(3), 509; https://doi.org/10.3390/s19030509
Received: 5 December 2018 / Revised: 14 January 2019 / Accepted: 22 January 2019 / Published: 26 January 2019
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Abstract
Mobility is a fundamental requirement for a healthy, active lifestyle. Gait analysis is widely acknowledged as a clinically useful tool for identifying problems with mobility, as identifying abnormalities within the gait profile is essential to correct them via training, drugs, or surgical intervention. [...] Read more.
Mobility is a fundamental requirement for a healthy, active lifestyle. Gait analysis is widely acknowledged as a clinically useful tool for identifying problems with mobility, as identifying abnormalities within the gait profile is essential to correct them via training, drugs, or surgical intervention. However, continuous gait analysis is difficult to achieve due to technical limitations, namely the need for specific hardware and constraints on time and test environment to acquire reliable data. Wearables may provide a solution if users carry them most of the time they are walking. We propose to add sensors to walking canes to assess user’s mobility. Canes are frequently used by people who cannot completely support their own weight due to pain or balance issues. Furthermore, in absence of neurological disorders, the load on the cane is correlated with the user condition. Sensorized canes already exist, but often rely on expensive sensors and major device modifications are required. Thus, the number of potential users is severely limited. In this work, we propose an affordable module for load monitoring so that it can be widely used as a screening tool. The main advantages of our module are: (i) it can be deployed in any standard cane with minimal changes that do not affect ergonomics; (ii) it can be used every day, anywhere for long-term monitoring. We have validated our prototype with 10 different elderly volunteers that required a cane to walk, either for balance or partial weight bearing. Volunteers were asked to complete a 10 m test and, then, to move freely for an extra minute. The load peaks on the cane, corresponding to maximum support instants during the gait cycle, were measured while they moved. For validation, we calculated their gait speed using a chronometer during the 10 m test, as it is reportedly related to their condition. The correlation between speed (condition) and load results proves that our module provides meaningful information for screening. In conclusion, our module monitors support in a continuous, unsupervised, nonintrusive way during users’ daily routines, plus only mechanical adjustment (cane height) is needed to change from one user to another. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Gradient-Based Multi-Objective Feature Selection for Gait Mode Recognition of Transfemoral Amputees
Sensors 2019, 19(2), 253; https://doi.org/10.3390/s19020253
Received: 3 November 2018 / Revised: 3 January 2019 / Accepted: 4 January 2019 / Published: 10 January 2019
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Abstract
One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We [...] Read more.
One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and minimum complexity for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14 % ± 1.51 % and 98.45 % ± 1.22 % with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Validation of an Inertial Sensor Algorithm to Quantify Head and Trunk Movement in Healthy Young Adults and Individuals with Mild Traumatic Brain Injury
Sensors 2018, 18(12), 4501; https://doi.org/10.3390/s18124501
Received: 20 October 2018 / Revised: 10 December 2018 / Accepted: 16 December 2018 / Published: 19 December 2018
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Abstract
Wearable inertial measurement units (IMUs) may provide useful, objective information to clinicians interested in quantifying head movements as patients’ progress through vestibular rehabilitation. The purpose of this study was to validate an IMU-based algorithm against criterion data (motion capture) to estimate average head [...] Read more.
Wearable inertial measurement units (IMUs) may provide useful, objective information to clinicians interested in quantifying head movements as patients’ progress through vestibular rehabilitation. The purpose of this study was to validate an IMU-based algorithm against criterion data (motion capture) to estimate average head and trunk range of motion (ROM) and average peak velocity. Ten participants completed two trials of standing and walking tasks while moving the head with and without moving the trunk. Validity was assessed using a combination of Intra-class Correlation Coefficients (ICC), root mean square error (RMSE), and percent error. Bland-Altman plots were used to assess bias. Excellent agreement was found between the IMU and criterion data for head ROM and peak rotational velocity (average ICC > 0.9). The trunk showed good agreement for most conditions (average ICC > 0.8). Average RMSE for both ROM (head = 2.64°; trunk = 2.48°) and peak rotational velocity (head = 11.76 °/s; trunk = 7.37 °/s) was low. The average percent error was below 5% for head and trunk ROM and peak rotational velocity. No clear pattern of bias was found for any measure across conditions. Findings suggest IMUs may provide a promising solution for estimating head and trunk movement, and a practical solution for tracking progression throughout rehabilitation or home exercise monitoring. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Comparison of Different Algorithms for Calculating Velocity and Stride Length in Running Using Inertial Measurement Units
Sensors 2018, 18(12), 4194; https://doi.org/10.3390/s18124194
Received: 30 August 2018 / Revised: 13 November 2018 / Accepted: 22 November 2018 / Published: 30 November 2018
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Abstract
Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this [...] Read more.
Running has a positive impact on human health and is an accessible sport for most people. There is high demand for tracking running performance and progress for amateurs and professionals alike. The parameters velocity and distance are thereby of main interest. In this work, we evaluate the accuracy of four algorithms, which calculate the stride velocity and stride length during running using data of an inertial measurement unit (IMU) placed in the midsole of a running shoe. The four algorithms are based on stride time, foot acceleration, foot trajectory estimation, and deep learning, respectively. They are compared using two studies: a laboratory-based study comprising 2377 strides from 27 subjects with 3D motion tracking as a reference and a field study comprising 12 subjects performing a 3.2-km run in a real-world setup. The results show that the foot trajectory estimation algorithm performs best, achieving a mean error of 0.032 ± 0.274 m/s for the velocity estimation and 0.022 ± 0.157 m for the stride length. An interesting alternative for systems with a low energy budget is the acceleration-based approach. Our results support the implementation decision for running velocity and distance tracking using IMUs embedded in the sole of a running shoe. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
Sensors 2018, 18(12), 4132; https://doi.org/10.3390/s18124132
Received: 14 September 2018 / Revised: 11 November 2018 / Accepted: 19 November 2018 / Published: 26 November 2018
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Abstract
Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the [...] Read more.
Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities is discussed. The six daily activities are walking, walking upstairs, walking downstairs, sitting, standing and lying. It involved three stages of activity recognition; namely, data signal processing (filtering and segmentation), feature extraction and classification. Five types of ensemble classifiers utilized are Bagging, Adaboost, Rotation forest, Ensembles of nested dichotomies (END) and Random subspace. These ensemble classifiers employed Support vector machine (SVM) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is evaluated with the holdout and 10-fold cross-validation evaluation methods. The performance of each human daily activity was measured in terms of precision, recall, F-measure, and receiver operating characteristic (ROC) curve. In addition, the performance is also measured based on the comparison of overall accuracy rate of classification between different ensemble classifiers and base learners. It was observed that overall, SVM produced better accuracy rate with 99.22% compared to RF with 97.91% based on a random subspace ensemble classifier. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
A Batteryless, Wireless Strain Sensor Using Resonant Frequency Modulation
Sensors 2018, 18(11), 3955; https://doi.org/10.3390/s18113955
Received: 20 September 2018 / Revised: 7 November 2018 / Accepted: 9 November 2018 / Published: 15 November 2018
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Abstract
In this study, we demonstrated the feasibility of a wireless strain sensor using resonant frequency modulation through tensile impedance test and wireless sensing test. To achieve a high stretchability, the sensor was fabricated by embedding a copper wire with high conductivity in a [...] Read more.
In this study, we demonstrated the feasibility of a wireless strain sensor using resonant frequency modulation through tensile impedance test and wireless sensing test. To achieve a high stretchability, the sensor was fabricated by embedding a copper wire with high conductivity in a silicone rubber with high stretchability, in which the resonant frequency can be modulated according to changes in strain. The characteristics of the sensor and the behavior of wireless sensing were calculated based on equations and simulated using finite element method. As the strain of the sensor increased, the inductance increased, resulting in the modulation of resonant frequency. In experimental measurement, as the strain of the sensor increased from 0% to 110%, its inductance was increased from 192 nH to 220 nH, changed by 14.5%, and the resonant frequency was shifted from 13.56 MHz to 12.72 MHz, decreased by 6.2%. It was demonstrated that using the proposed sensor, strains up to 110% could be detected wirelessly up to a few centimeters. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
A Unified Deep-Learning Model for Classifying the Cross-Country Skiing Techniques Using Wearable Gyroscope Sensors
Sensors 2018, 18(11), 3819; https://doi.org/10.3390/s18113819
Received: 31 August 2018 / Revised: 26 October 2018 / Accepted: 4 November 2018 / Published: 7 November 2018
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Abstract
The automatic classification of cross-country (XC) skiing techniques using data from wearable sensors has the potential to provide insights for optimizing the performance of professional skiers. In this paper, we propose a unified deep learning model for classifying eight techniques used in classical [...] Read more.
The automatic classification of cross-country (XC) skiing techniques using data from wearable sensors has the potential to provide insights for optimizing the performance of professional skiers. In this paper, we propose a unified deep learning model for classifying eight techniques used in classical and skating styles XC-skiing and optimize this model for the number of gyroscope sensors by analyzing the results for five different configurations of sensors. We collected data of four professional skiers on outdoor flat and natural courses. The model is first trained over the flat course data of two skiers and tested over the flat and natural course data of a third skier in a leave-one-out fashion, resulting in a mean accuracy of ~80% over three combinations. Secondly, the model is trained over the flat course data of three skiers and tested over flat course and natural course data of one new skier, resulting in a mean accuracy of 87.2% and 95.1% respectively, using the optimal sensor configuration (five gyroscope sensors: both hands, both feet, and the pelvis). High classification accuracy obtained using both approaches indicates that this deep learning model has the potential to be deployed for real-time classification of skiing techniques by professional skiers and coaches. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
The Design and Application of Simplified Insole-Based Prototypes with Plantar Pressure Measurement for Fast Screening of Flat-Foot
Sensors 2018, 18(11), 3617; https://doi.org/10.3390/s18113617
Received: 30 August 2018 / Revised: 16 October 2018 / Accepted: 20 October 2018 / Published: 25 October 2018
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Abstract
This study aimed to find the correlation between conventional Arch Index (AI) measurements and our prototype of a simplified insole-based plantar pressure measurement system and to find out the effective plantar pressure sensor position. Twenty-one subjects participated in this study, which was divided [...] Read more.
This study aimed to find the correlation between conventional Arch Index (AI) measurements and our prototype of a simplified insole-based plantar pressure measurement system and to find out the effective plantar pressure sensor position. Twenty-one subjects participated in this study, which was divided into two groups: 10 subjects with flatfoot and 11 subjects with normal foot. Five force sensitive resistance sensors were used on this prototype using Arduino as the data acquisition device. Two types of trials, namely static and dynamic, were conducted to validate our system against the ink-type AI measurement as a golden standard. The results showed that in the static trial, there was a high linear correlation with the medial arch sensor configuration, while in the dynamic trial, there was a high linear correlation in the medial arch sensor configuration and sensor 5 configuration. This study showed that both static and dynamic tests using the self-developed device could effectively determine most of the flatfoot subjects and suggests that in the future, it can be applied in clinical applications because of its advantages when compared to the expensive-high tech graphic input board and conventional tools, like ink-type based measurements. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Context Impacts in Accelerometer-Based Walk Detection and Step Counting
Sensors 2018, 18(11), 3604; https://doi.org/10.3390/s18113604
Received: 31 August 2018 / Revised: 28 September 2018 / Accepted: 10 October 2018 / Published: 24 October 2018
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Abstract
Walk detection (WD) and step counting (SC) have become popular applications in the recent emergence of wearable devices. These devices monitor user states and process data from MEMS-based accelerometers and optional gyroscope sensors. Various algorithms have been proposed for WD and SC, which [...] Read more.
Walk detection (WD) and step counting (SC) have become popular applications in the recent emergence of wearable devices. These devices monitor user states and process data from MEMS-based accelerometers and optional gyroscope sensors. Various algorithms have been proposed for WD and SC, which are generally sensitive to the contexts of applications, i.e., (1) the locations of sensor placement; (2) the sensor orientations; (3) the user’s walking patterns; (4) the preprocessing window sizes; and (5) the sensor sampling rates. A thorough understanding of how these dynamic factors affect the algorithms’ performances is investigated and compared in this paper. In particular, representative WD and SC algorithms are introduced according to their design methodologies. A series of experiments is designed in consideration of different application contexts to form an experimental dataset. Different algorithms are then implemented and evaluated on the dataset. The evaluation results provide a quantitative performance comparison indicating the advantages and weaknesses of different algorithms under different application scenarios, giving valuable guidance for algorithm selection in practical applications. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Gait Study of Parkinson’s Disease Subjects Using Haptic Cues with A Motorized Walker
Sensors 2018, 18(10), 3549; https://doi.org/10.3390/s18103549
Received: 11 September 2018 / Revised: 16 October 2018 / Accepted: 17 October 2018 / Published: 19 October 2018
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Abstract
Gait abnormalities are one of the distinguishing symptoms of patients with Parkinson’s disease (PD) that contribute to fall risk. Our study compares the gait parameters of people with PD when they walk through a predefined course under different haptic speed cue conditions (1) [...] Read more.
Gait abnormalities are one of the distinguishing symptoms of patients with Parkinson’s disease (PD) that contribute to fall risk. Our study compares the gait parameters of people with PD when they walk through a predefined course under different haptic speed cue conditions (1) without assistance, (2) pushing a conventional rolling walker, and (3) holding onto a self-navigating motorized walker under different speed cues. Six people with PD were recruited at the New York Institute of Technology College of Osteopathic Medicine to participate in this study. Spatial posture and gait data of the test subjects were collected via a VICON motion capture system. We developed a framework to process and extract gait features and applied statistical analysis on these features to examine the significance of the findings. The results showed that the motorized walker providing a robust haptic cue significantly improved gait symmetry of PD subjects. Specifically, the asymmetry index of the gait cycle time was reduced from 6.7% when walking without assistance to 0.56% and below when using a walker. Furthermore, the double support time of a gait cycle was reduced by 4.88% compared to walking without assistance. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson’s Disease
Sensors 2018, 18(10), 3523; https://doi.org/10.3390/s18103523
Received: 7 September 2018 / Revised: 5 October 2018 / Accepted: 15 October 2018 / Published: 18 October 2018
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Abstract
A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson’s Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for [...] Read more.
A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson’s Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and clinical UPDRS scores of patient performance is used to assess new task instances by a machine learning approach based on supervised classifiers. The classifiers have been trained by an experimental campaign on cohorts of PD patients. Experimental results show that automated assessments of the system replicate clinical ones, demonstrating its effectiveness in home monitoring of PD. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
A Fuzzy Tuned and Second Estimator of the Optimal Quaternion Complementary Filter for Human Motion Measurement with Inertial and Magnetic Sensors
Sensors 2018, 18(10), 3517; https://doi.org/10.3390/s18103517
Received: 4 September 2018 / Revised: 10 October 2018 / Accepted: 16 October 2018 / Published: 18 October 2018
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Abstract
To accurately measure human motion at high-speed, we proposed a simple structure complementary filter, named the Fuzzy Tuned and Second EStimator of the Optimal Quaternion Complementary Filter (FTECF). The FTECF is applicable to inertial and magnetic sensors, which include tri-axis gyroscopes, tri-axis accelerometers, [...] Read more.
To accurately measure human motion at high-speed, we proposed a simple structure complementary filter, named the Fuzzy Tuned and Second EStimator of the Optimal Quaternion Complementary Filter (FTECF). The FTECF is applicable to inertial and magnetic sensors, which include tri-axis gyroscopes, tri-axis accelerometers, and tri-axis magnetometers. More specifically, the proposed method incorporates three parts, the input quaternion, the reference quaternion, and the fuzzy logic algorithm. At first, the input quaternion was calculated with gyroscopes. Then, the reference quaternion was calculated by applying the Second EStimator of the Optimal Quaternion (ESOQ-2) algorithm on accelerometers and magnetometers. In addition, we added compensation for accelerometers in the ESOQ-2 algorithm so as to eliminate the effects of limb motion acceleration in high-speed human motion measurements. Finally, the fuzzy logic was utilized to calculate the fusion factor for a complementary filter, so as to adaptively fuse the input quaternion with the reference quaternion. Additionally, the overall algorithm design is more simplified than traditional methods. Confirmed by the experiments, using a commercial inertial and magnetic sensors unit and an optical motion capture system, the efficiency of the proposed method was more improved than two well-known methods. The root mean square error (RMSE) of the FTECF was less than 2.2° and the maximum error was less than 5.4°. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Inertial Sensor Angular Velocities Reflect Dynamic Knee Loading during Single Limb Loading in Individuals Following Anterior Cruciate Ligament Reconstruction
Sensors 2018, 18(10), 3460; https://doi.org/10.3390/s18103460
Received: 6 September 2018 / Revised: 2 October 2018 / Accepted: 3 October 2018 / Published: 15 October 2018
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Abstract
Difficulty quantifying knee loading deficits clinically in individuals following anterior cruciate ligament reconstruction (ACLr) may underlie their persistence. Expense associated with quantifying knee moments (KMom) and power (KPow) with gold standard techniques precludes their use in the clinic. As segment and joint kinematics [...] Read more.
Difficulty quantifying knee loading deficits clinically in individuals following anterior cruciate ligament reconstruction (ACLr) may underlie their persistence. Expense associated with quantifying knee moments (KMom) and power (KPow) with gold standard techniques precludes their use in the clinic. As segment and joint kinematics are used to calculate moments and power, it is possible that more accessible inertial sensor technology can be used to identify knee loading deficits. However, it is unknown if angular velocities measured with inertial sensors provide meaningful information regarding KMom/KPow during dynamic tasks post-ACLr. Twenty-one individuals 5.1 ± 1.5 months post-ACLr performed a single limb loading task, bilaterally. Data collected concurrently using a marker-based motion system and gyroscopes positioned lateral thighs/shanks. Intraclass correlation coefficients (ICC)(2,k) determined concurrent validity. To determine predictive ability of angular velocities for KMom/KPow, separate stepwise linear regressions performed using peak thigh, shank, and knee angular velocities extracted from gyroscopes. ICCs were greater than 0.947 (p < 0.001) for all variables. Thigh (r = 0.812 and r = 0.585; p < 0.001) and knee (r = 0.806 and r = 0.536; p < 0.001) angular velocities were strongly and moderately correlated to KPow and KMom, respectively. High ICCs indicated strong agreement between measurement systems. Thigh angular velocity (R2 = 0.66; p < 0.001) explained 66% of variance in KPow suggesting gyroscopes provide meaningful information regarding KPow. Less expensive inertial sensors may be helpful in identifying deficits clinically. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Scanning Laser Rangefinders for the Unobtrusive Monitoring of Gait Parameters in Unsupervised Settings
Sensors 2018, 18(10), 3424; https://doi.org/10.3390/s18103424
Received: 9 August 2018 / Revised: 9 October 2018 / Accepted: 10 October 2018 / Published: 12 October 2018
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Abstract
Since variations in common gait parameters (such as cadence, velocity and stride-length) of elderly people are a reliable indicator of functional and cognitive decline in aging and increased fall risks, such gait parameters have to be monitored continuously to enable preventive interventions as [...] Read more.
Since variations in common gait parameters (such as cadence, velocity and stride-length) of elderly people are a reliable indicator of functional and cognitive decline in aging and increased fall risks, such gait parameters have to be monitored continuously to enable preventive interventions as early as possible. With scanning laser rangefinders (SLR) having been shown to be suitable for standardised (frontal) gait assessments, this article introduces an unobtrusive gait monitoring (UGMO) system for lateral gait monitoring in homes for the elderly. The system has been evaluated in comparison to a GAITRite (as reference system) with 86 participants (ranging from 21 to 82 years) passing the 6-min walk test twice. Within the considered 56,351 steps within an overall 7877 walks and approximately 34 km distance travelled, it has been shown that the SLR Hokuyo UST10-LX is more sensitive than the cheaper URG-04LX version in regard to the correct (automatic) detection of lateral steps (98% compared to 77%) and walks (97% compared to 66%). Furthermore, it has been confirmed that the UGMO (with the SLR UST10-LX) can measure gait parameters such as gait velocity and stride length with sufficient sensitivity to determine age- and disease-related functional (and cognitive) decline. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Multiple-Wearable-Sensor-Based Gait Classification and Analysis in Patients with Neurological Disorders
Sensors 2018, 18(10), 3397; https://doi.org/10.3390/s18103397
Received: 23 August 2018 / Revised: 29 September 2018 / Accepted: 6 October 2018 / Published: 11 October 2018
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Abstract
The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: [...] Read more.
The aim of this study was to conduct a comprehensive analysis of the placement of multiple wearable sensors for the purpose of analyzing and classifying the gaits of patients with neurological disorders. Seven inertial measurement unit (IMU) sensors were placed at seven locations: the lower back (L5) and both sides of the thigh, distal tibia (shank), and foot. The 20 subjects selected to participate in this study were separated into two groups: stroke patients (11) and patients with neurological disorders other than stroke (brain concussion, spinal injury, or brain hemorrhage) (9). The temporal parameters of gait were calculated using a wearable device, and various features and sensor configurations were examined to establish the ideal accuracy for classifying different groups. A comparison of the various methods and features for classifying the three groups revealed that a combination of time domain and gait temporal feature-based classification with the Multilayer Perceptron (MLP) algorithm outperformed the other methods of feature-based classification. The classification results of different sensor placements revealed that the sensor placed on the shank achieved higher accuracy than the other sensor placements (L5, foot, and thigh). The placement-based classification of the shank sensor achieved 89.13% testing accuracy with the Decision Tree (DT) classifier algorithm. The results of this study indicate that the wearable IMU device is capable of differentiating between the gait patterns of healthy patients, patients with stroke, and patients with other neurological disorders. Moreover, the most favorable results were reported for the classification that used the combination of time domain and gait temporal features as the model input and the shank location for sensor placement. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Gait Symmetry Assessment with a Low Back 3D Accelerometer in Post-Stroke Patients
Sensors 2018, 18(10), 3322; https://doi.org/10.3390/s18103322
Received: 14 August 2018 / Revised: 15 September 2018 / Accepted: 29 September 2018 / Published: 3 October 2018
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Abstract
Gait asymmetry is an important marker of mobility impairment post stroke. This study proposes a new gait symmetry index (GSI) to quantify gait symmetry with one 3D accelerometer at L3 (GSIL3). GSIL3 was evaluated with 16 post stroke patients and [...] Read more.
Gait asymmetry is an important marker of mobility impairment post stroke. This study proposes a new gait symmetry index (GSI) to quantify gait symmetry with one 3D accelerometer at L3 (GSIL3). GSIL3 was evaluated with 16 post stroke patients and nine healthy controls in the Six-Minute-Walk-Test (6-MWT). Discriminative power was evaluated with Wilcoxon test and the effect size (ES) was computed with Cliff’s Delta. GSIL3 estimated during the entire 6-MWT and during a short segment straight walk (GSIL3straight) have comparable effect size to one another (ES = 0.89, p < 0.001) and to the symmetry indices derived from feet sensors (|ES| = [0.22, 0.89]). Furthermore, while none of the indices derived from feet sensors showed significant differences between post stroke patients walking with a cane compared to those able to walk without, GSIL3 was able to discriminate between these two groups with a significantly lower value in the group using a cane (ES = 0.70, p = 0.02). In addition, GSIL3 was strongly associated with several symmetry indices measured by feet sensors during the straight walking cycles (Spearman correlation: |ρ| = [0.82, 0.88], p < 0.05). The proposed index can be a reliable and cost-efficient post stroke gait symmetry assessment with implications for research and clinical practice. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements
Sensors 2018, 18(10), 3310; https://doi.org/10.3390/s18103310
Received: 30 July 2018 / Revised: 24 September 2018 / Accepted: 29 September 2018 / Published: 2 October 2018
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Abstract
One of the most common assessments for the mobility of older people is the Timed Up and Go test (TUG). Due to its sensitivity regarding the indication of Parkinson’s disease (PD) or increased fall risk in elderly people, this assessment test becomes increasingly [...] Read more.
One of the most common assessments for the mobility of older people is the Timed Up and Go test (TUG). Due to its sensitivity regarding the indication of Parkinson’s disease (PD) or increased fall risk in elderly people, this assessment test becomes increasingly relevant, should be automated and should become applicable for unsupervised self-assessments to enable regular examinations of the functional status. With Inertial Measurement Units (IMU) being well suited for automated analyses, we evaluate an IMU-based analysis-system, which automatically detects the TUG execution via machine learning and calculates the test duration. as well as the duration of its single components. The complete TUG was classified with an accuracy of 96% via a rule-based model in a study with 157 participants aged over 70 years. A comparison between the TUG durations determined by IMU and criterion standard measurements (stopwatch and automated/ambient TUG (aTUG) system) showed significant correlations of 0.97 and 0.99, respectively. The classification of the instrumented TUG (iTUG)-components achieved accuracies over 96%, as well. Additionally, the system’s suitability for self-assessments was investigated within a semi-unsupervised situation where a similar movement sequence to the TUG was executed. This preliminary analysis confirmed that the self-selected speed correlates moderately with the speed in the test situation, but differed significantly from each other. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Examination of the Effect of Suitable Size of Shoes under the Second Metatarsal Head and Width of Shoes under the Fifth Metatarsal Head for the Prevention of Callus Formation in Healthy Young Women
Sensors 2018, 18(10), 3269; https://doi.org/10.3390/s18103269
Received: 22 August 2018 / Revised: 23 September 2018 / Accepted: 27 September 2018 / Published: 28 September 2018
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Abstract
Excessive pressure and shear stress while walking cause a risk of callus formation, which eventually causes foot ulcers in patients with diabetes mellitus. Callus under the second metatarsal head (MTH) has been associated with increased shear stress/pressure ratios (SPR). Callus under the fifth [...] Read more.
Excessive pressure and shear stress while walking cause a risk of callus formation, which eventually causes foot ulcers in patients with diabetes mellitus. Callus under the second metatarsal head (MTH) has been associated with increased shear stress/pressure ratios (SPR). Callus under the fifth MTH has been associated with increased peak shear stress (PSS). The purpose of this study is to examine whether the effect of the suitable size and width of shoes prevents diabetic foot ulcers under the second and fifth MTH. We measured the pressure and shear stress by testing three kinds of sizes and two types of width of shoes. Significant difference was not observed in the SPR under the second MTH among different sizes of shoes. However, the pressure and shear stress were significantly lower when putting on shoes of fit size compared with larger sizes. The PSS under the fifth MTH was significantly smaller when putting on shoes of fit width compared with those of narrow width. Wearing shoes of fit size and width has the potential to prevent callus formation by reducing the pressure and shear stress constituting SPR under the second MTH and PSS under the fifth MTH. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach
Sensors 2018, 18(9), 2828; https://doi.org/10.3390/s18092828
Received: 27 June 2018 / Revised: 21 August 2018 / Accepted: 23 August 2018 / Published: 27 August 2018
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Abstract
Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients [...] Read more.
Wearable sensors can provide detailed information on human movement but the clinical impact of this information remains limited. We propose a machine learning approach, using wearable sensor data, to identify subject-specific changes in gait patterns related to improvements in clinical outcomes. Eight patients with knee osteoarthritis (OA) completed two gait trials before and one following an exercise intervention. Wearable sensor data (e.g., 3-dimensional (3D) linear accelerations) were collected from a sensor located near the lower back, lateral thigh and lateral shank during level treadmill walking at a preferred speed. Wearable sensor data from the 2 pre-intervention gait trials were used to define each individual’s typical movement pattern using a one-class support vector machine (OCSVM). The percentage of strides defined as outliers, based on the pre-intervention gait data and the OCSVM, were used to define the overall change in an individual’s movement pattern. The correlation between the change in movement patterns following the intervention (i.e., percentage of outliers) and improvement in self-reported clinical outcomes (e.g., pain and function) was assessed using a Spearman rank correlation. The number of outliers observed post-intervention exhibited a large association (ρ = 0.78) with improvements in self-reported clinical outcomes. These findings demonstrate a proof-of-concept and a novel methodological approach for integrating machine learning and wearable sensor data. This approach provides an objective and evidence-informed way to understand clinically important changes in human movement patterns in response to exercise therapy. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System
Sensors 2018, 18(9), 2743; https://doi.org/10.3390/s18092743
Received: 12 July 2018 / Revised: 16 August 2018 / Accepted: 18 August 2018 / Published: 21 August 2018
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Abstract
Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the [...] Read more.
Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients’ health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
A Novel and Safe Approach to Simulate Cutting Movements Using Ground Reaction Forces
Sensors 2018, 18(8), 2631; https://doi.org/10.3390/s18082631
Received: 1 June 2018 / Revised: 6 August 2018 / Accepted: 9 August 2018 / Published: 11 August 2018
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Abstract
Control of shear ground reaction forces (sGRF) is important in performing running and cutting tasks as poor sGRF control has implications for those with knee injuries, such as anterior cruciate ligament (ACL) ruptures. The goal of this study was to develop a novel [...] Read more.
Control of shear ground reaction forces (sGRF) is important in performing running and cutting tasks as poor sGRF control has implications for those with knee injuries, such as anterior cruciate ligament (ACL) ruptures. The goal of this study was to develop a novel and safe task to evaluate control or accurate modulation of shear ground reaction forces related to those generated during cutting. Our approach utilized a force control task using real-time visual feedback of a subject’s force production and evaluated control capabilities through accuracy and divergence measurements. Ten healthy recreational athletes completed the force control task while force control via accuracy measures and divergence calculations was investigated. Participants were able to accurately control sGRF in multiple directions based on error measurements. Forces generated during the task were equal to or greater than those measured during a number of functional activities. We found no significant difference in the divergence of the force profiles using the Lyapunov Exponent of the sGRF trajectories. Participants using our approach produced high accuracy and low divergence force profiles and functional force magnitudes. Moving forward, we will utilize this task in at-risk populations who are unable to complete a cutting maneuver in early stages of rehabilitation, such as ACL deficient and newly reconstructed individuals, allowing insight into force control not obtainable otherwise. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection
Sensors 2018, 18(6), 1918; https://doi.org/10.3390/s18061918
Received: 9 April 2018 / Revised: 28 May 2018 / Accepted: 11 June 2018 / Published: 12 June 2018
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Abstract
Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, [...] Read more.
Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including Multiple Cameras Fall (with 2 classes and 3 classes) and UR Fall Detection achieving percentage accuracies of 99.2, 99.25 and 99.0, respectively. Comparisons with nine state-of-the-art methods demonstrate the effectiveness of the proposed approach on both datasets. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessFeature PaperArticle
Inertial Sensor-Based Variables Are Indicators of Frailty and Adverse Post-Operative Outcomes in Cardiovascular Disease Patients
Sensors 2018, 18(6), 1792; https://doi.org/10.3390/s18061792
Received: 26 February 2018 / Revised: 31 May 2018 / Accepted: 31 May 2018 / Published: 2 June 2018
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Abstract
Cardiovascular disease (CVD) patients with intrinsic cardiac cause for falling have been found to be frail and submissive to morbidity and mortality as post-operative outcomes. In these older CVD patients, gait speed is conjectured by the Society of Thoracic Surgeons (STS) as an [...] Read more.
Cardiovascular disease (CVD) patients with intrinsic cardiac cause for falling have been found to be frail and submissive to morbidity and mortality as post-operative outcomes. In these older CVD patients, gait speed is conjectured by the Society of Thoracic Surgeons (STS) as an independent predictor of post-operative morbidity and mortality. However, this guideline by STS has not been studied adequately with a large sample size; rather it is based largely on expert opinions of cardiac surgeons and researchers. Although one’s gait speed is not completely associated with one’s risk of falls, gait speed is a quick robust measure to classify frail/non-frail CVD patients and undoubtedly frail individuals are more prone to falls. Thus, this study examines the effects of inertial sensor-based quick movement variability characteristics in identifying CVD patients likely to have an adverse post-operative outcome. This study establishes a relationship with gait and postural predictor variables with patient’s post-operative adverse outcomes. Accordingly, inertial sensors embedded inside smartphones are indispensable for the assessment of elderly patients in clinical environments and may be necessary for quick objective assessment. Sixteen elderly CVD patients (Age 76.1 ± 3.6 years) who were scheduled for cardiac surgery the next day were recruited for this study. Based on STS recommendation guidelines, eight of the CVD patients were classified as frail (prone to adverse outcomes with gait speed ≤ 0.833 m/s) and the other eight patients as non-frail (gait speed > 0.833 m/s). Smartphone-derived walking velocity was found to be significantly lower in frail patients than that in non-frail patients (p < 0.01). Mean Center of Pressure (COP) radius (p < 0.01), COP Area (p < 0.01), COP path length (p < 0.05) and mean COP velocity (p < 0.05) were found to be significantly higher in frail patients than that in the non-frail patient group. Nonlinear variability measures such as sample entropy were significantly lower in frail participants in anterior-posterior (p < 0.01) and resultant sway direction (p < 0.01) than in the non-frail group. This study identified numerous postural and movement variability parameters that offer insights into predictive inertial sensor-based variables and post-operative adverse outcomes among CVD patients. In future, smartphone-based clinical measurement systems could serve as a clinical decision support system for assessing patients quickly in the perioperative period. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Toward Smart Footwear to Track Frailty Phenotypes—Using Propulsion Performance to Determine Frailty
Sensors 2018, 18(6), 1763; https://doi.org/10.3390/s18061763
Received: 31 March 2018 / Revised: 25 May 2018 / Accepted: 25 May 2018 / Published: 1 June 2018
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Abstract
Frailty assessment is dependent on the availability of trained personnel and it is currently limited to clinic and supervised setting. The growing aging population has made it necessary to find phenotypes of frailty that can be measured in an unsupervised setting for translational [...] Read more.
Frailty assessment is dependent on the availability of trained personnel and it is currently limited to clinic and supervised setting. The growing aging population has made it necessary to find phenotypes of frailty that can be measured in an unsupervised setting for translational application in continuous, remote, and in-place monitoring during daily living activity, such as walking. We analyzed gait performance of 161 older adults using a shin-worn inertial sensor to investigate the feasibility of developing a foot-worn sensor to assess frailty. Sensor-derived gait parameters were extracted and modeled to distinguish different frailty stages, including non-frail, pre-frail, and frail, as determined by Fried Criteria. An artificial neural network model was implemented to evaluate the accuracy of an algorithm using a proposed set of gait parameters in predicting frailty stages. Changes in discriminating power was compared between sensor data extracted from the left and right shin sensor. The aim was to investigate the feasibility of developing a foot-worn sensor to assess frailty. The results yielded a highly accurate model in predicting frailty stages, irrespective of sensor location. The independent predictors of frailty stages were propulsion duration and acceleration, heel-off and toe-off speed, mid stance and mid swing speed, and speed norm. The proposed model enables discriminating different frailty stages with area under curve ranging between 83.2–95.8%. Furthermore, results from the neural network suggest the potential of developing a single-shin worn sensor that would be ideal for unsupervised application and footwear integration for continuous monitoring during walking. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method
Sensors 2018, 18(6), 1737; https://doi.org/10.3390/s18061737
Received: 3 April 2018 / Revised: 24 May 2018 / Accepted: 25 May 2018 / Published: 28 May 2018
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Abstract
Accurate estimation of gait parameters is essential for obtaining quantitative information on motor deficits in Parkinson’s disease and other neurodegenerative diseases, which helps determine disease progression and therapeutic interventions. Due to the demand for high accuracy, unobtrusive measurement methods such as optical motion [...] Read more.
Accurate estimation of gait parameters is essential for obtaining quantitative information on motor deficits in Parkinson’s disease and other neurodegenerative diseases, which helps determine disease progression and therapeutic interventions. Due to the demand for high accuracy, unobtrusive measurement methods such as optical motion capture systems, foot pressure plates, and other systems have been commonly used in clinical environments. However, the high cost of existing lab-based methods greatly hinders their wider usage, especially in developing countries. In this study, we present a low-cost, noncontact, and an accurate temporal gait parameters estimation method by sensing and analyzing the electrostatic field generated from human foot stepping. The proposed method achieved an average 97% accuracy on gait phase detection and was further validated by comparison to the foot pressure system in 10 healthy subjects. Two results were compared using the Pearson coefficient r and obtained an excellent consistency (r = 0.99, p < 0.05). The repeatability of the purposed method was calculated between days by intraclass correlation coefficients (ICC), and showed good test-retest reliability (ICC = 0.87, p < 0.01). The proposed method could be an affordable and accurate tool to measure temporal gait parameters in hospital laboratories and in patients’ home environments. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessFeature PaperArticle
Dynamical Properties of Postural Control in Obese Community-Dwelling Older Adults
Sensors 2018, 18(6), 1692; https://doi.org/10.3390/s18061692
Received: 10 March 2018 / Revised: 19 April 2018 / Accepted: 22 May 2018 / Published: 24 May 2018
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Abstract
Postural control is a key aspect in preventing falls. The aim of this study was to determine if obesity affected balance in community-dwelling older adults and serve as an indicator of fall risk. The participants were randomly assigned to receive a comprehensive geriatric [...] Read more.
Postural control is a key aspect in preventing falls. The aim of this study was to determine if obesity affected balance in community-dwelling older adults and serve as an indicator of fall risk. The participants were randomly assigned to receive a comprehensive geriatric assessment followed by a longitudinal assessment of their fall history. The standing postural balance was measured for 98 participants with a Body Mass Index (BMI) ranging from 18 to 63 kg/m2, using a force plate and an inertial measurement unit affixed at the sternum. Participants’ fall history was recorded over 2 years and participants with at least one fall in the prior year were classified as fallers. The results suggest that body weight/BMI is an additional risk factor for falling in elderly persons and may be an important marker for fall risk. The linear variables of postural analysis suggest that the obese fallers have significantly higher sway area and sway ranges, along with higher root mean square and standard deviation of time series. Additionally, it was found that obese fallers have lower complexity of anterior-posterior center of pressure time series. Future studies should examine more closely the combined effect of aging and obesity on dynamic balance. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors
Sensors 2018, 18(5), 1639; https://doi.org/10.3390/s18051639
Received: 9 April 2018 / Revised: 14 May 2018 / Accepted: 18 May 2018 / Published: 21 May 2018
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Abstract
Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics [...] Read more.
Biometrics is currently an area that is both very interesting as well as rapidly growing. Among various types of biometrics the human gait recognition seems to be one of the most intriguing. However, one of the greatest problems within this field of biometrics is the change in gait caused by footwear. A change of shoes results in a significant lowering of accuracy in recognition of people. The following work presents a method which uses data gathered by two sensors: force plates and Microsoft Kinect v2 to reduce this problem. Microsoft Kinect is utilized to measure the body height of a person which allows the reduction of the set of recognized people only to those whose height is similar to that which has been measured. The entire process is preceded by identifying the type of footwear which the person is wearing. The research was conducted on data obtained from 99 people (more than 3400 strides) and the proposed method allowed us to reach a Correct Classification Rate (CCR) greater than 88% which, in comparison to earlier methods reaching CCR’s of <80%, is a significant improvement. The work presents advantages as well as limitations of the proposed method. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Biomechanical Gait Variable Estimation Using Wearable Sensors after Unilateral Total Knee Arthroplasty
Sensors 2018, 18(5), 1577; https://doi.org/10.3390/s18051577
Received: 7 April 2018 / Revised: 4 May 2018 / Accepted: 11 May 2018 / Published: 15 May 2018
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Abstract
Total knee arthroplasty is a common surgical treatment for end-stage osteoarthritis of the knee. The majority of existing studies that have explored the relationship between recovery and gait biomechanics have been conducted in laboratory settings. However, seamless gait parameter monitoring in real-world conditions [...] Read more.
Total knee arthroplasty is a common surgical treatment for end-stage osteoarthritis of the knee. The majority of existing studies that have explored the relationship between recovery and gait biomechanics have been conducted in laboratory settings. However, seamless gait parameter monitoring in real-world conditions may provide a better understanding of recovery post-surgery. The purpose of this study was to estimate kinematic and kinetic gait variables using two ankle-worn wearable sensors in individuals after unilateral total knee arthroplasty. Eighteen subjects at least six months post-unilateral total knee arthroplasty participated in this study. Four biomechanical gait variables were measured using an instrumented split-belt treadmill and motion capture systems. Concurrently, eleven inertial gait variables were extracted from two ankle-worn accelerometers. Subsets of the inertial gait variables for each biomechanical gait variable estimation were statistically selected. Then, hierarchical regressions were created to determine the directional contributions of the inertial gait variables for biomechanical gait variable estimations. Selected inertial gait variables significantly predicted trial-averaged biomechanical gait variables. Moreover, strong directionally-aligned relationships were observed. Wearable-based gait monitoring of multiple and sequential kinetic gait variables in daily life could provide a more accurate understanding of the relationships between movement patterns and recovery from total knee arthroplasty. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
A Telerehabilitation System for the Selection, Evaluation and Remote Management of Therapies
Sensors 2018, 18(5), 1459; https://doi.org/10.3390/s18051459
Received: 16 March 2018 / Revised: 30 April 2018 / Accepted: 4 May 2018 / Published: 8 May 2018
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Abstract
Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by [...] Read more.
Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TrhOnt, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Improving Fall Detection Using an On-Wrist Wearable Accelerometer
Sensors 2018, 18(5), 1350; https://doi.org/10.3390/s18051350
Received: 12 March 2018 / Revised: 18 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are [...] Read more.
Fall detection is a very important challenge that affects both elderly people and the carers. Improvements in fall detection would reduce the aid response time. This research focuses on a method for fall detection with a sensor placed on the wrist. Falls are detected using a published threshold-based solution, although a study on threshold tuning has been carried out. The feature extraction is extended in order to balance the dataset for the minority class. Alternative models have been analyzed to reduce the computational constraints so the solution can be embedded in smart-phones or smart wristbands. Several published datasets have been used in the Materials and Methods section. Although these datasets do not include data from real falls of elderly people, a complete comparison study of fall-related datasets shows statistical differences between the simulated falls and real falls from participants suffering from impairment diseases. Given the obtained results, the rule-based systems represent a promising research line as they perform similarly to neural networks, but with a reduced computational cost. Furthermore, support vector machines performed with a high specificity. However, further research to validate the proposal in real on-line scenarios is needed. Furthermore, a slight improvement should be made to reduce the number of false alarms. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Wearable Sensors and the Assessment of Frailty among Vulnerable Older Adults: An Observational Cohort Study
Sensors 2018, 18(5), 1336; https://doi.org/10.3390/s18051336
Received: 12 March 2018 / Revised: 18 April 2018 / Accepted: 24 April 2018 / Published: 26 April 2018
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Abstract
Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty [...] Read more.
Background: The geriatric syndrome of frailty is one of the greatest challenges facing the U.S. aging population. Frailty in older adults is associated with higher adverse outcomes, such as mortality and hospitalization. Identifying precise early indicators of pre-frailty and measures of specific frailty components are of key importance to enable targeted interventions and remediation. We hypothesize that sensor-derived parameters, measured by a pendant accelerometer device in the home setting, are sensitive to identifying pre-frailty. Methods: Using the Fried frailty phenotype criteria, 153 community-dwelling, ambulatory older adults were classified as pre-frail (51%), frail (22%), or non-frail (27%). A pendant sensor was used to monitor the at home physical activity, using a chest acceleration over 48 h. An algorithm was developed to quantify physical activity pattern (PAP), physical activity behavior (PAB), and sleep quality parameters. Statistically significant parameters were selected to discriminate the pre-frail from frail and non-frail adults. Results: The stepping parameters, walking parameters, PAB parameters (sedentary and moderate-to-vigorous activity), and the combined parameters reached and area under the curve of 0.87, 0.85, 0.85, and 0.88, respectively, for identifying pre-frail adults. No sleep parameters discriminated the pre-frail from the rest of the adults. Conclusions: This study demonstrates that a pendant sensor can identify pre-frailty via daily home monitoring. These findings may open new opportunities in order to remotely measure and track frailty via telehealth technologies. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Gait Shear and Plantar Pressure Monitoring: A Non-Invasive OFS Based Solution for e-Health Architectures
Sensors 2018, 18(5), 1334; https://doi.org/10.3390/s18051334
Received: 26 March 2018 / Revised: 12 April 2018 / Accepted: 20 April 2018 / Published: 25 April 2018
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Abstract
In an era of unprecedented progress in sensing technology and communication, health services are now able to closely monitor patients and elderly citizens without jeopardizing their daily routines through health applications on their mobile devices in what is known as e-Health. Within this [...] Read more.
In an era of unprecedented progress in sensing technology and communication, health services are now able to closely monitor patients and elderly citizens without jeopardizing their daily routines through health applications on their mobile devices in what is known as e-Health. Within this field, we propose an optical fiber sensor (OFS) based system for the simultaneous monitoring of shear and plantar pressure during gait movement. These parameters are considered to be two key factors in gait analysis that can help in the early diagnosis of multiple anomalies, such as diabetic foot ulcerations or in physical rehabilitation scenarios. The proposed solution is a biaxial OFS based on two in-line fiber Bragg gratings (FBGs), which were inscribed in the same optical fiber and placed individually in two adjacent cavities, forming a small sensing cell. Such design presents a more compact and resilient solution with higher accuracy when compared to the existing electronic systems. The implementation of the proposed elements into an insole is also described, showcasing the compactness of the sensing cells, which can easily be integrated into a non-invasive mobile e-Health solution for continuous remote gait monitoring of patients and elder citizens. The reported results show that the proposed system outperforms existing solutions, in the sense that it is able to dynamically discriminate shear and plantar pressure during gait. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Older Adults with Weaker Muscle Strength Stand up from a Sitting Position with More Dynamic Trunk Use
Sensors 2018, 18(4), 1235; https://doi.org/10.3390/s18041235
Received: 16 March 2018 / Revised: 8 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
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Abstract
The ability to stand up from a sitting position is essential for older adults to live independently. Body-fixed inertial sensors may provide an approach for quantifying the sit-to-stand (STS) in clinical settings. The aim of this study was to determine whether measurements of [...] Read more.
The ability to stand up from a sitting position is essential for older adults to live independently. Body-fixed inertial sensors may provide an approach for quantifying the sit-to-stand (STS) in clinical settings. The aim of this study was to determine whether measurements of STS movements using body-fixed sensors yield parameters that are informative regarding changes in STS performance in older adults with reduced muscle strength. In twenty-seven healthy older adults, handgrip strength was assessed as a proxy for overall muscle strength. Subjects were asked to stand up from a chair placed at three heights. Trunk movements were measured using an inertial sensor fixed to the back. Duration, angular range, and maximum angular velocity of STS phases, as well as the vertical velocity of the extension phase, were calculated. Backwards elimination using Generalized Estimating Equations was used to determine if handgrip strength predicted the STS durations and trunk kinematics. Weaker subjects (i.e., with lower handgrip strength) were slower during the STS and showed a larger flexion angular range and a larger extension angular range. In addition, weaker subjects showed a greater maximum angular velocity, which increased with lower seat heights. Measurements with a single inertial sensor did reveal that older adults with lower handgrip strength employed a different strategy to stand up from a sitting position, involving more dynamic use of the trunk. This effect was greatest when elevating body mass. Trunk kinematic parameters were more sensitive to reduced muscle strength than durations. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessFeature PaperArticle
“What Is a Step?” Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research
Sensors 2018, 18(4), 1206; https://doi.org/10.3390/s18041206
Received: 28 February 2018 / Revised: 8 April 2018 / Accepted: 12 April 2018 / Published: 15 April 2018
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Abstract
(1) Background: This study compared manually-counted treadmill walking steps from the hip-worn DigiwalkerSW200 and OmronHJ720ITC, and hip and wrist-worn ActiGraph GT3X+ and GT9X; determined brand-specific acceleration amplitude (g) and/or frequency (Hz) step-detection thresholds; and quantified key features of the acceleration signal during walking. [...] Read more.
(1) Background: This study compared manually-counted treadmill walking steps from the hip-worn DigiwalkerSW200 and OmronHJ720ITC, and hip and wrist-worn ActiGraph GT3X+ and GT9X; determined brand-specific acceleration amplitude (g) and/or frequency (Hz) step-detection thresholds; and quantified key features of the acceleration signal during walking. (2) Methods: Twenty participants (Age: 26.7 ± 4.9 years) performed treadmill walking between 0.89-to-1.79 m/s (2–4 mph) while wearing a hip-worn DigiwalkerSW200, OmronHJ720ITC, GT3X+ and GT9X, and a wrist-worn GT3X+ and GT9X. A DigiwalkerSW200 and OmronHJ720ITC underwent shaker testing to determine device-specific frequency and amplitude step-detection thresholds. Simulated signal testing was used to determine thresholds for the ActiGraph step algorithm. Steps during human testing were compared using bias and confidence intervals. (3) Results: The OmronHJ720ITC was most accurate during treadmill walking. Hip and wrist-worn ActiGraph outputs were significantly different from the criterion. The DigiwalkerSW200 records steps for movements with a total acceleration of ≥1.21 g. The OmronHJ720ITC detects a step when movement has an acceleration ≥0.10 g with a dominant frequency of ≥1 Hz. The step-threshold for the ActiLife algorithm is variable based on signal frequency. Acceleration signals at the hip and wrist have distinctive patterns during treadmill walking. (4) Conclusions: Three common research-grade physical activity monitors employ different step-detection strategies, which causes variability in step output. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning
Sensors 2018, 18(4), 1155; https://doi.org/10.3390/s18041155
Received: 15 February 2018 / Revised: 3 April 2018 / Accepted: 4 April 2018 / Published: 10 April 2018
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Abstract
This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing [...] Read more.
This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA). Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Motor Subtypes of Parkinson’s Disease Can Be Identified by Frequency Component of Postural Stability
Sensors 2018, 18(4), 1102; https://doi.org/10.3390/s18041102
Received: 26 February 2018 / Revised: 31 March 2018 / Accepted: 4 April 2018 / Published: 5 April 2018
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Abstract
Parkinson’s disease (PD) can be divided into two subtypes based on clinical features—namely tremor dominant (TD) and postural instability and gait difficulty (PIGD). This categorization is important at the early stage of PD, since identifying the subtypes can help to predict the clinical [...] Read more.
Parkinson’s disease (PD) can be divided into two subtypes based on clinical features—namely tremor dominant (TD) and postural instability and gait difficulty (PIGD). This categorization is important at the early stage of PD, since identifying the subtypes can help to predict the clinical progression of the disease. Accordingly, correctly diagnosing subtypes is critical in initiating appropriate early interventions and tracking the progression of the disease. However, as the disease progresses, it becomes increasingly difficult to further distinguish those attributes that are relevant to the subtypes. In this study, we investigated whether a method using the standing center of pressure (COP) time series data can separate two subtypes of PD by looking at the frequency component of COP (i.e., COP position and speed). Thirty-six participants diagnosed with PD were evaluated, with their bare feet on the force platform, and were instructed to stand upright with their arms by their sides for 20 s (with their eyes open and closed), which is consistent with the traditional COP measures. Fast Fourier transform (FFT) and wavelet transform (WT) were performed to distinguish between the motor subtypes using the COP measures. The TD group exhibited larger amplitudes at the frequency range of 3–7 Hz when compared to the PIGD group. Both the FFT and WT methods were able to differentiate the subtypes. COP time series information can be used to differentiate between the two motor subtypes of PD, using the frequency component of postural stability. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
The Height-Adaptive Parameterized Step Length Measurement Method and Experiment Based on Motion Parameters
Sensors 2018, 18(4), 1039; https://doi.org/10.3390/s18041039
Received: 8 February 2018 / Revised: 14 March 2018 / Accepted: 22 March 2018 / Published: 30 March 2018
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Abstract
In order to tackle the inaccurate step length measurement of people with different heights and in different motion states, a height-adaptive method of step length measurement based on motion parameters is proposed in this paper. This method takes people’s height, stride frequency, and [...] Read more.
In order to tackle the inaccurate step length measurement of people with different heights and in different motion states, a height-adaptive method of step length measurement based on motion parameters is proposed in this paper. This method takes people’s height, stride frequency, and changing accelerometer output while walking into integrated consideration, and builds a dynamic and parameterized model of their step length. In this study, these parameters were calibrated with thirty sets of experiment data from people with different heights and in different motion states, which were then verified experimentally by motion data of randomly selected subjects, regardless of speed and height. The experiment results indicate that the height-adaptive step length measurement was realized, thus eliminating the influence of height exerted on step length measurement. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Nondestructive Estimation of Muscle Contributions to STS Training with Different Loadings Based on Wearable Sensor System
Sensors 2018, 18(4), 971; https://doi.org/10.3390/s18040971
Received: 26 January 2018 / Revised: 20 March 2018 / Accepted: 21 March 2018 / Published: 25 March 2018
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Abstract
Partial body weight support or loading sit-to-stand (STS) rehabilitation can be useful for persons with lower limb dysfunction to achieve movement again based on the internal residual muscle force and external assistance. To explicate how the muscles contribute to the kinetics and kinematics [...] Read more.
Partial body weight support or loading sit-to-stand (STS) rehabilitation can be useful for persons with lower limb dysfunction to achieve movement again based on the internal residual muscle force and external assistance. To explicate how the muscles contribute to the kinetics and kinematics of STS performance by non-invasive in vitro detection and to nondestructively estimate the muscle contributions to STS training with different loadings, a wearable sensor system was developed with ground reaction force (GRF) platforms, motion capture inertial sensors and electromyography (EMG) sensors. To estimate the internal moments of hip, knee and ankle joints and quantify the contributions of individual muscle and gravity to STS movement, the inverse dynamics analysis on a simplified STS biomechanical model with external loading is proposed. The functional roles of the lower limb individual muscles (rectus femoris (RF), gluteus maximus (GM), vastus lateralis (VL), tibialis anterior (TA) and gastrocnemius (GAST)) during STS motion and the mechanism of the muscles’ synergies to perform STS-specific subtasks were analyzed. The muscle contributions to the biomechanical STS subtasks of vertical propulsion, anteroposterior (AP) braking and propulsion for body balance in the sagittal plane were quantified by experimental studies with EMG, kinematic and kinetic data. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Motor Planning Error: Toward Measuring Cognitive Frailty in Older Adults Using Wearables
Sensors 2018, 18(3), 926; https://doi.org/10.3390/s18030926
Received: 18 January 2018 / Revised: 5 March 2018 / Accepted: 16 March 2018 / Published: 20 March 2018
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Abstract
Practical tools which can be quickly administered are needed for measuring subtle changes in cognitive–motor performance over time. Frailty together with cognitive impairment, or ‘cognitive frailty’, are shown to be strong and independent predictors of cognitive decline over time. We have developed an [...] Read more.
Practical tools which can be quickly administered are needed for measuring subtle changes in cognitive–motor performance over time. Frailty together with cognitive impairment, or ‘cognitive frailty’, are shown to be strong and independent predictors of cognitive decline over time. We have developed an interactive instrumented trail-making task (iTMT) platform, which allows quantification of motor planning error (MPE) through a series of ankle reaching tasks. In this study, we examined the accuracy of MPE in identifying cognitive frailty in older adults. Thirty-two older adults (age = 77.3 ± 9.1 years, body-mass-index = 25.3 ± 4.7 kg/m2, female = 38%) were recruited. Using either the Mini-Mental State Examination or Montreal Cognitive Assessment (MoCA), 16 subjects were classified as cognitive-intact and 16 were classified as cognitive-impaired. In addition, 12 young-healthy subjects (age = 26.0 ± 5.2 years, body-mass-index = 25.3 ± 3.9 kg/m2, female = 33%) were recruited to establish a healthy benchmark. Subjects completed the iTMT, using an ankle-worn sensor, which transforms ankle motion into navigation of a computer cursor. The iTMT task included reaching five indexed target circles (including numbers 1-to-3 and letters A&B placed in random order) on the computer-screen by moving the ankle-joint while standing. The ankle-sensor quantifies MPE through analysis of the pattern of ankle velocity. MPE was defined as percentage of time deviation between subject’s maximum ankle velocity and the optimal maximum ankle velocity, which is halfway through the reaching pathway. Data from gait tests, including single task and dual task walking, were also collected to determine cognitive–motor performance. The average MPE in young-healthy, elderly cognitive-intact, and elderly cognitive-impaired groups was 11.1 ± 5.7%, 20.3 ± 9.6%, and 34.1 ± 4.2% (p < 0.001), respectively. Large effect sizes (Cohen’s d = 1.17–4.56) were observed for discriminating between groups using MPE. Significant correlations were observed between the MPE and MoCA score (r = −0.670, p < 0.001) as well as between the MPE and dual task stride velocity (r = −0.584, p < 0.001). This study demonstrated feasibility and efficacy of estimating MPE from a practical wearable platform with promising results in identifying cognitive–motor impairment and potential application in assessing cognitive frailty. The proposed platform could be also used as an alternative to dual task walking test, where gait assessment may not be practical. Future studies need to confirm these observations in larger samples. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Inertial Measurement Units for Clinical Movement Analysis: Reliability and Concurrent Validity
Sensors 2018, 18(3), 719; https://doi.org/10.3390/s18030719
Received: 15 January 2018 / Revised: 10 February 2018 / Accepted: 26 February 2018 / Published: 28 February 2018
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Abstract
The aim of this study was to investigate the reliability and concurrent validity of a commercially available Xsens MVN BIOMECH inertial-sensor-based motion capture system during clinically relevant functional activities. A clinician with no prior experience of motion capture technologies and an experienced clinical [...] Read more.
The aim of this study was to investigate the reliability and concurrent validity of a commercially available Xsens MVN BIOMECH inertial-sensor-based motion capture system during clinically relevant functional activities. A clinician with no prior experience of motion capture technologies and an experienced clinical movement scientist each assessed 26 healthy participants within each of two sessions using a camera-based motion capture system and the MVN BIOMECH system. Participants performed overground walking, squatting, and jumping. Sessions were separated by 4 ± 3 days. Reliability was evaluated using intraclass correlation coefficient and standard error of measurement, and validity was evaluated using the coefficient of multiple correlation and the linear fit method. Day-to-day reliability was generally fair-to-excellent in all three planes for hip, knee, and ankle joint angles in all three tasks. Within-day (between-rater) reliability was fair-to-excellent in all three planes during walking and squatting, and poor-to-high during jumping. Validity was excellent in the sagittal plane for hip, knee, and ankle joint angles in all three tasks and acceptable in frontal and transverse planes in squat and jump activity across joints. Our results suggest that the MVN BIOMECH system can be used by a clinician to quantify lower-limb joint angles in clinically relevant movements. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
An Affordable Insole-Sensor-Based Trans-Femoral Prosthesis for Normal Gait
Sensors 2018, 18(3), 706; https://doi.org/10.3390/s18030706
Received: 18 January 2018 / Revised: 15 February 2018 / Accepted: 19 February 2018 / Published: 27 February 2018
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Abstract
This paper proposes a novel and an affordable lower limb prosthesis to enable normal gait kinematics for trans-femoral amputees. The paper details the design of a passive prosthesis with magneto-rheological (MR) damping system and electronic control. A new control approach based on plantar [...] Read more.
This paper proposes a novel and an affordable lower limb prosthesis to enable normal gait kinematics for trans-femoral amputees. The paper details the design of a passive prosthesis with magneto-rheological (MR) damping system and electronic control. A new control approach based on plantar insole feedback was employed here. Strategically placed sensors on the plantar insole provide required information about gait cycle to a finite state controller for suitable action. A proportional integral (PI) based current controller controls the required current for necessary damping during gait. The prosthesis was designed and developed locally in India keeping in view the cost, functionality, socio-economic, and aesthetic requirements. The prototype was experimentally tested on a trans-femoral amputee and the results are presented in this work. The implementation of the proposed design and control scheme in the prototype successfully realizes the notion that normal gait kinematics can be achieved at a low cost comparable to passive prostheses. The incurring cost and power expenditure of the proposed prosthesis are evaluated against passive and active prostheses, respectively. The commercial implications for the prosthesis were explored on the basis of recommendations of ISPO Consensus Conference on Appropriate Prosthetic Technology in Developing Countries. The key objective of this work is to enable lucid design for development of an affordable prosthesis in a low-resource setting. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
An Automatic Gait Feature Extraction Method for Identifying Gait Asymmetry Using Wearable Sensors
Sensors 2018, 18(2), 676; https://doi.org/10.3390/s18020676
Received: 6 January 2018 / Revised: 8 February 2018 / Accepted: 20 February 2018 / Published: 24 February 2018
Cited by 4 | PDF Full-text (6824 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method [...] Read more.
This paper aims to assess the use of Inertial Measurement Unit (IMU) sensors to identify gait asymmetry by extracting automatic gait features. We design and develop an android app to collect real time synchronous IMU data from legs. The results from our method are validated using a Qualisys Motion Capture System. The data are collected from 10 young and 10 older subjects. Each performed a trial in a straight corridor comprising 15 strides of normal walking, a turn around and another 15 strides. We analyse the data for total distance, total time, total velocity, stride, step, cadence, step ratio, stance, and swing. The accuracy of detecting the stride number using the proposed method is 100% for young and 92.67% for older subjects. The accuracy of estimating travelled distance using the proposed method for young subjects is 97.73% and 98.82% for right and left legs; and for the older, is 88.71% and 89.88% for right and left legs. The average travelled distance is 37.77 (95% CI ± 3.57) meters for young subjects and is 22.50 (95% CI ± 2.34) meters for older subjects. The average travelled time for young subjects is 51.85 (95% CI ± 3.08) seconds and for older subjects is 84.02 (95% CI ± 9.98) seconds. The results show that wearable sensors can be used for identifying gait asymmetry without the requirement and expense of an elaborate laboratory setup. This can serve as a tool in diagnosing gait abnormalities in individuals and opens the possibilities for home based self-gait asymmetry assessment. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Estimation of Handgrip Force from SEMG Based on Wavelet Scale Selection
Sensors 2018, 18(2), 663; https://doi.org/10.3390/s18020663
Received: 15 January 2018 / Revised: 17 February 2018 / Accepted: 22 February 2018 / Published: 24 February 2018
Cited by 2 | PDF Full-text (5821 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the [...] Read more.
This paper proposes a nonlinear correlation-based wavelet scale selection technology to select the effective wavelet scales for the estimation of handgrip force from surface electromyograms (SEMG). The SEMG signal corresponding to gripping force was collected from extensor and flexor forearm muscles during the force-varying analysis task. We performed a computational sensitivity analysis on the initial nonlinear SEMG-handgrip force model. To explore the nonlinear correlation between ten wavelet scales and handgrip force, a large-scale iteration based on the Monte Carlo simulation was conducted. To choose a suitable combination of scales, we proposed a rule to combine wavelet scales based on the sensitivity of each scale and selected the appropriate combination of wavelet scales based on sequence combination analysis (SCA). The results of SCA indicated that the scale combination VI is suitable for estimating force from the extensors and the combination V is suitable for the flexors. The proposed method was compared to two former methods through prolonged static and force-varying contraction tasks. The experiment results showed that the root mean square errors derived by the proposed method for both static and force-varying contraction tasks were less than 20%. The accuracy and robustness of the handgrip force derived by the proposed method is better than that obtained by the former methods. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
A Real-Time Wireless Sweat Rate Measurement System for Physical Activity Monitoring
Sensors 2018, 18(2), 533; https://doi.org/10.3390/s18020533
Received: 20 October 2017 / Revised: 26 January 2018 / Accepted: 8 February 2018 / Published: 10 February 2018
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Abstract
There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant [...] Read more.
There has been significant research on the physiology of sweat in the past decade, with one of the main interests being the development of a real-time hydration monitor that utilizes sweat. The contents of sweat have been known for decades; sweat provides significant information on the physiological condition of the human body. However, it is important to know the sweat rate as well, as sweat rate alters the concentration of the sweat constituents, and ultimately affects the accuracy of hydration detection. Towards this goal, a calorimetric based flow-rate detection system was built and tested to determine sweat rate in real time. The proposed sweat rate monitoring system has been validated through both controlled lab experiments (syringe pump) and human trials. An Internet of Things (IoT) platform was embedded, with the sensor using a Simblee board and Raspberry Pi. The overall prototype is capable of sending sweat rate information in real time to either a smartphone or directly to the cloud. Based on a proven theoretical concept, our overall system implementation features a pioneer device that can truly measure the rate of sweat in real time, which was tested and validated on human subjects. Our realization of the real-time sweat rate watch is capable of detecting sweat rates as low as 0.15 µL/min/cm2, with an average error in accuracy of 18% compared to manual sweat rate readings. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Self-Tuning Threshold Method for Real-Time Gait Phase Detection Based on Ground Contact Forces Using FSRs
Sensors 2018, 18(2), 481; https://doi.org/10.3390/s18020481
Received: 8 December 2017 / Revised: 20 January 2018 / Accepted: 31 January 2018 / Published: 6 February 2018
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Abstract
This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait [...] Read more.
This paper presents a novel methodology for detecting the gait phase of human walking on level ground. The previous threshold method (TM) sets a threshold to divide the ground contact forces (GCFs) into on-ground and off-ground states. However, the previous methods for gait phase detection demonstrate no adaptability to different people and different walking speeds. Therefore, this paper presents a self-tuning triple threshold algorithm (STTTA) that calculates adjustable thresholds to adapt to human walking. Two force sensitive resistors (FSRs) were placed on the ball and heel to measure GCFs. Three thresholds (i.e., high-threshold, middle-threshold andlow-threshold) were used to search out the maximum and minimum GCFs for the self-adjustments of thresholds. The high-threshold was the main threshold used to divide the GCFs into on-ground and off-ground statuses. Then, the gait phases were obtained through the gait phase detection algorithm (GPDA), which provides the rules that determine calculations for STTTA. Finally, the STTTA reliability is determined by comparing the results between STTTA and Mariani method referenced as the timing analysis module (TAM) and Lopez–Meyer methods. Experimental results show that the proposed method can be used to detect gait phases in real time and obtain high reliability when compared with the previous methods in the literature. In addition, the proposed method exhibits strong adaptability to different wearers walking at different walking speeds. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Flexible Piezoelectric Sensor-Based Gait Recognition
Sensors 2018, 18(2), 468; https://doi.org/10.3390/s18020468
Received: 2 January 2018 / Revised: 1 February 2018 / Accepted: 3 February 2018 / Published: 5 February 2018
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Abstract
Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The [...] Read more.
Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Estimation of Foot Plantar Center of Pressure Trajectories with Low-Cost Instrumented Insoles Using an Individual-Specific Nonlinear Model
Sensors 2018, 18(2), 421; https://doi.org/10.3390/s18020421
Received: 29 November 2017 / Revised: 24 January 2018 / Accepted: 30 January 2018 / Published: 1 February 2018
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Abstract
Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, [...] Read more.
Postural control is a complex skill based on the interaction of dynamic sensorimotor processes, and can be challenging for people with deficits in sensory functions. The foot plantar center of pressure (COP) has often been used for quantitative assessment of postural control. Previously, the foot plantar COP was mainly measured by force plates or complicated and expensive insole-based measurement systems. Although some low-cost instrumented insoles have been developed, their ability to accurately estimate the foot plantar COP trajectory was not robust. In this study, a novel individual-specific nonlinear model was proposed to estimate the foot plantar COP trajectories with an instrumented insole based on low-cost force sensitive resistors (FSRs). The model coefficients were determined by a least square error approximation algorithm. Model validation was carried out by comparing the estimated COP data with the reference data in a variety of postural control assessment tasks. We also compared our data with the COP trajectories estimated by the previously well accepted weighted mean approach. Comparing with the reference measurements, the average root mean square errors of the COP trajectories of both feet were 2.23 mm (±0.64) (left foot) and 2.72 mm (±0.83) (right foot) along the medial–lateral direction, and 9.17 mm (±1.98) (left foot) and 11.19 mm (±2.98) (right foot) along the anterior–posterior direction. The results are superior to those reported in previous relevant studies, and demonstrate that our proposed approach can be used for accurate foot plantar COP trajectory estimation. This study could provide an inexpensive solution to fall risk assessment in home settings or community healthcare center for the elderly. It has the potential to help prevent future falls in the elderly. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
What is the Best Configuration of Wearable Sensors to Measure Spatiotemporal Gait Parameters in Children with Cerebral Palsy?
Sensors 2018, 18(2), 394; https://doi.org/10.3390/s18020394
Received: 13 December 2017 / Revised: 23 January 2018 / Accepted: 25 January 2018 / Published: 30 January 2018
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Abstract
Wearable inertial devices have recently been used to evaluate spatiotemporal parameters of gait in daily life situations. Given the heterogeneity of gait patterns in children with cerebral palsy (CP), the sensor placement and analysis algorithm may influence the validity of the results. This [...] Read more.
Wearable inertial devices have recently been used to evaluate spatiotemporal parameters of gait in daily life situations. Given the heterogeneity of gait patterns in children with cerebral palsy (CP), the sensor placement and analysis algorithm may influence the validity of the results. This study aimed at comparing the spatiotemporal measurement performances of three wearable configurations defined by different sensor positioning on the lower limbs: (1) shanks and thighs, (2) shanks, and (3) feet. The three configurations were selected based on their potential to be used in daily life for children with CP and typically developing (TD) controls. For each configuration, dedicated gait analysis algorithms were used to detect gait events and compute spatiotemporal parameters. Fifteen children with CP and 11 TD controls were included. Accuracy, precision, and agreement of the three configurations were determined in comparison with an optoelectronic system as a reference. The three configurations were comparable for the evaluation of TD children and children with a low level of disability (CP-GMFCS I) whereas the shank-and-thigh-based configuration was more robust regarding children with a higher level of disability (CP-GMFCS II–III). Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
The Effect of the Accelerometer Operating Range on Biomechanical Parameters: Stride Length, Velocity, and Peak Tibial Acceleration during Running
Sensors 2018, 18(1), 130; https://doi.org/10.3390/s18010130
Received: 11 November 2017 / Revised: 28 December 2017 / Accepted: 4 January 2018 / Published: 5 January 2018
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Abstract
Previous studies have used accelerometers with various operating ranges (ORs) when measuring biomechanical parameters. However, it is still unclear whether ORs influence the accuracy of running parameters, and whether the different stiffnesses of footwear midsoles influence this accuracy. The purpose of the present [...] Read more.
Previous studies have used accelerometers with various operating ranges (ORs) when measuring biomechanical parameters. However, it is still unclear whether ORs influence the accuracy of running parameters, and whether the different stiffnesses of footwear midsoles influence this accuracy. The purpose of the present study was to systematically investigate the influence of OR on the accuracy of stride length, running velocity, and on peak tibial acceleration. Twenty-one recreational heel strike runners ran on a 15-m indoor track at self-selected running speeds in three footwear conditions (low to high midsole stiffness). Runners were equipped with an inertial measurement unit (IMU) affixed to the heel cup of the right shoe and with a uniaxial accelerometer at the right tibia. Accelerometers (at the tibia and included in the IMU) with a high OR of ±70 g were used as the reference and the data were cut at ±32, ±16, and at ±8 g in post-processing, before calculating parameters. The results show that the OR influenced the outcomes of all investigated parameters, which were not influenced by tested footwear conditions. The lower ORs were associated with an underestimation error for all biomechanical parameters, which increased noticeably with a decreasing OR. It can be concluded that accelerometers with a minimum OR of ±32 g should be used to avoid inaccurate measurements. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
An Advanced Hybrid Technique of DCS and JSRC for Telemonitoring of Multi-Sensor Gait Pattern
Sensors 2017, 17(12), 2764; https://doi.org/10.3390/s17122764
Received: 25 October 2017 / Revised: 21 November 2017 / Accepted: 28 November 2017 / Published: 29 November 2017
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Abstract
The jointly quantitative analysis of multi-sensor gait data for the best gait-classification performance has been a challenging endeavor in wireless body area networks (WBANs)-based gait telemonitoring applications. In this study, based on the joint sparsity of data, we proposed an advanced hybrid technique [...] Read more.
The jointly quantitative analysis of multi-sensor gait data for the best gait-classification performance has been a challenging endeavor in wireless body area networks (WBANs)-based gait telemonitoring applications. In this study, based on the joint sparsity of data, we proposed an advanced hybrid technique of distributed compressed sensing (DCS) and joint sparse representation classification (JSRC) for multi-sensor gait classification. Firstly, the DCS technique is utilized to simultaneously compress multi-sensor gait data for capturing spatio-temporal correlation information about gait while the energy efficiency of the sensors is available. Then, the jointly compressed gait data are directly used to develop a novel neighboring sample-based JSRC model by defining the sparse representation coefficients-inducing criterion (SRCC), in order to yield the best classification performance as well as a lower computational time cost. The multi-sensor gait data were selected from an open wearable action recognition database (WARD) to validate the feasibility of our proposed method. The results showed that when the comparison ratio and the number of neighboring samples are selected as 70% and 40%, respectively, the best accuracy (95%) can be reached while the lowest computational time spends only 60 ms. Moreover, the best accuracy and the computational time can increase by 5% and decrease by 40 ms, respectively, when compared with the traditional JSRC techniques. Our proposed hybrid technique can take advantage of the joint sparsity of data for jointly processing multi-sensor gait data, which greatly contributes to the best gait-classification performance. This has great potential for energy-efficient telemonitoring of multi-sensor gait. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Three-Axis Ground Reaction Force Distribution during Straight Walking
Sensors 2017, 17(10), 2431; https://doi.org/10.3390/s17102431
Received: 30 August 2017 / Revised: 12 October 2017 / Accepted: 19 October 2017 / Published: 24 October 2017
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Abstract
We measured the three-axis ground reaction force (GRF) distribution during straight walking. Small three-axis force sensors composed of rubber and sensor chips were fabricated and calibrated. After sensor calibration, 16 force sensors were attached to the left shoe. The three-axis force distribution during [...] Read more.
We measured the three-axis ground reaction force (GRF) distribution during straight walking. Small three-axis force sensors composed of rubber and sensor chips were fabricated and calibrated. After sensor calibration, 16 force sensors were attached to the left shoe. The three-axis force distribution during straight walking was measured, and the local features of the three-axis force under the sole of the shoe were analyzed. The heel area played a role in receiving the braking force, the base area of the fourth and fifth toes applied little vertical or shear force, the base area of the second and third toes generated a portion of the propulsive force and received a large vertical force, and the base area of the big toe helped move the body’s center of mass to the other foot. The results demonstrate that measuring the three-axis GRF distribution is useful for a detailed analysis of bipedal locomotion. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessArticle
Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns
Sensors 2017, 17(10), 2274; https://doi.org/10.3390/s17102274
Received: 30 August 2017 / Revised: 28 September 2017 / Accepted: 4 October 2017 / Published: 5 October 2017
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Abstract
In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier [...] Read more.
In this paper, we develop and validate a new algorithm to detect steps while walking at speeds between 30 and 40 steps per minute based on the data sensed from a single tri-axial accelerometer. The algorithm concatenates three consecutive phases. First, an outlier detection is performed on the sensed data based on the Mahalanobis distance to pre-detect candidate points in the acceleration time series that may contain a ground contact segment of data while walking. Second, the acceleration segment around the pre-detected point is used to calculate the transition matrix in order to capture the time dependencies. Finally, autoencoders, trained with data segments containing ground contact transition matrices from acceleration series from labeled steps are used to reconstruct the computed transition matrices at each pre-detected point. A similarity index is used to assess if the pre-selected point contains a true step in the 30–40 steps per minute speed range. Our experimental results, based on a database from three different participants performing similar activities to the target one, are able to achieve a recall = 0.88 with precision = 0.50 improving the results when directly applying the autoencoders to acceleration patterns (recall = 0.77 with precision = 0.50). Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Review

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Open AccessFeature PaperReview
Deep Learning to Predict Falls in Older Adults Based on Daily-Life Trunk Accelerometry
Sensors 2018, 18(5), 1654; https://doi.org/10.3390/s18051654
Received: 31 March 2018 / Revised: 13 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
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Abstract
Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment [...] Read more.
Early detection of high fall risk is an essential component of fall prevention in older adults. Wearable sensors can provide valuable insight into daily-life activities; biomechanical features extracted from such inertial data have been shown to be of added value for the assessment of fall risk. Body-worn sensors such as accelerometers can provide valuable insight into fall risk. Currently, biomechanical features derived from accelerometer data are used for the assessment of fall risk. Here, we studied whether deep learning methods from machine learning are suited to automatically derive features from raw accelerometer data that assess fall risk. We used an existing dataset of 296 older adults. We compared the performance of three deep learning model architectures (convolutional neural network (CNN), long short-term memory (LSTM) and a combination of these two (ConvLSTM)) to each other and to a baseline model with biomechanical features on the same dataset. The results show that the deep learning models in a single-task learning mode are strong in recognition of identity of the subject, but that these models only slightly outperform the baseline method on fall risk assessment. When using multi-task learning, with gender and age as auxiliary tasks, deep learning models perform better. We also found that preprocessing of the data resulted in the best performance (AUC = 0.75). We conclude that deep learning models, and in particular multi-task learning, effectively assess fall risk on the basis of wearable sensor data. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessReview
Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review
Sensors 2018, 18(5), 1613; https://doi.org/10.3390/s18051613
Received: 29 January 2018 / Revised: 20 April 2018 / Accepted: 15 May 2018 / Published: 18 May 2018
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Abstract
In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms [...] Read more.
In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessReview
Shoe-Insole Technology for Injury Prevention in Walking
Sensors 2018, 18(5), 1468; https://doi.org/10.3390/s18051468
Received: 29 March 2018 / Revised: 23 April 2018 / Accepted: 29 April 2018 / Published: 8 May 2018
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Abstract
Impaired walking increases injury risk during locomotion, including falls-related acute injuries and overuse damage to lower limb joints. Gait impairments seriously restrict voluntary, habitual engagement in injury prevention activities, such as recreational walking and exercise. There is, therefore, an urgent need for technology-based [...] Read more.
Impaired walking increases injury risk during locomotion, including falls-related acute injuries and overuse damage to lower limb joints. Gait impairments seriously restrict voluntary, habitual engagement in injury prevention activities, such as recreational walking and exercise. There is, therefore, an urgent need for technology-based interventions for gait disorders that are cost effective, willingly taken-up, and provide immediate positive effects on walking. Gait control using shoe-insoles has potential as an effective population-based intervention, and new sensor technologies will enhance the effectiveness of these devices. Shoe-insole modifications include: (i) ankle joint support for falls prevention; (ii) shock absorption by utilising lower-resilience materials at the heel; (iii) improving reaction speed by stimulating cutaneous receptors; and (iv) preserving dynamic balance via foot centre of pressure control. Using sensor technology, such as in-shoe pressure measurement and motion capture systems, gait can be precisely monitored, allowing us to visualise how shoe-insoles change walking patterns. In addition, in-shoe systems, such as pressure monitoring and inertial sensors, can be incorporated into the insole to monitor gait in real-time. Inertial sensors coupled with in-shoe foot pressure sensors and global positioning systems (GPS) could be used to monitor spatiotemporal parameters in real-time. Real-time, online data management will enable ‘big-data’ applications to everyday gait control characteristics. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessReview
Technology-Based Feedback and Its Efficacy in Improving Gait Parameters in Patients with Abnormal Gait: A Systematic Review
Sensors 2018, 18(1), 142; https://doi.org/10.3390/s18010142
Received: 11 October 2017 / Revised: 14 December 2017 / Accepted: 2 January 2018 / Published: 6 January 2018
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Abstract
This systematic review synthesized and analyzed clinical findings related to the effectiveness of innovative technological feedback for tackling functional gait recovery. An electronic search of PUBMED, PEDro, WOS, CINAHL, and DIALNET was conducted from January 2011 to December 2016. The main inclusion criteria [...] Read more.
This systematic review synthesized and analyzed clinical findings related to the effectiveness of innovative technological feedback for tackling functional gait recovery. An electronic search of PUBMED, PEDro, WOS, CINAHL, and DIALNET was conducted from January 2011 to December 2016. The main inclusion criteria were: patients with modified or abnormal gait; application of technology-based feedback to deal with functional recovery of gait; any comparison between different kinds of feedback applied by means of technology, or any comparison between technological and non-technological feedback; and randomized controlled trials. Twenty papers were included. The populations were neurological patients (75%), orthopedic and healthy subjects. All participants were adults, bar one. Four studies used exoskeletons, 6 load platforms and 5 pressure sensors. The breakdown of the type of feedback used was as follows: 60% visual, 40% acoustic and 15% haptic. 55% used terminal feedback versus 65% simultaneous feedback. Prescriptive feedback was used in 60% of cases, while 50% used descriptive feedback. 62.5% and 58.33% of the trials showed a significant effect in improving step length and speed, respectively. Efficacy in improving other gait parameters such as balance or range of movement is observed in more than 75% of the studies with significant outcomes. Conclusion: Treatments based on feedback using innovative technology in patients with abnormal gait are mostly effective in improving gait parameters and therefore useful for the functional recovery of patients. The most frequently highlighted types of feedback were immediate visual feedback followed by terminal and immediate acoustic feedback. Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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Open AccessCorrection
Correction: Rucco, R.; et al. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors 2018, 18, 1613
Sensors 2018, 18(8), 2462; https://doi.org/10.3390/s18082462
Received: 23 July 2018 / Revised: 26 July 2018 / Accepted: 27 July 2018 / Published: 30 July 2018
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Abstract
The authors wish to make a correction to their paper [1]. The following Table 1 should be replaced with the table shown below it[...] Full article
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
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