sensors-logo

Journal Browser

Journal Browser

Special Issue "Wearable Sensors for Gait and Motion Analysis 2018"

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

Deadline for manuscript submissions: closed (31 July 2019).

Special Issue Editors

Prof. Dr. Shigeru Tadano

Guest Editor
National Institute of Technology, Hakodate College, Hakodatate, Japan and Hokkaido University, Sapporo, Japan
Interests: biomechanical engineering; musculo-skeletal and orthopaedic biomechanics; bone mechanics; medical and healthcare engineering
Special Issues and Collections in MDPI journals
Prof. Dr. Laura Gastaldi
Website
Guest Editor
Department of Mathematical Sciences, Politecnico di Torino, Torino, Italy
Interests: biomechanical engineering; gait analysis; motion analysis; adaptive sports; Rehabilitation; medical and healthcare engineering
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Wearable sensors are increasingly used to perform human gait and motion measurements. Some key issues of this success are their features of unobtrusiveness, light-weight, possibility to be used out of the lab, low costs and ease of use.

Wearable sensors were initially employed as diagnostic and monitoring tools for gait analysis, both to assess spatio-temporal gait parameters and joint kinematics. Nowadays, their main applications are still in the healthcare field, but new potential applications are emerging: Sport activities, e-health, tele-rehabilitation, elderly monitoring and wellness. More in general, all the activities that directly or indirectly involve motion might benefit from wearable sensors systems.

Wearable sensor-based systems can measure kinematic variables of a single or multiple body segments of the subject during motion. Although many researches have been reported on this topic, some issues associated to the reconstruction and analysis of the kinematics during motion are still an open challenge for the scientific community, especially in those fields that require high accuracy. Robust protocols and data post-processing are still work in progress, especially in cases in which there can be a high variability of motion patterns.

We invite original research papers and review articles aimed at proposing new kinds of wearable gait sensor systems, new methods for sensor signal processing, reports on applications in healthcare field, innovative and non-traditional motion analysis applications.

Contributions may include, but are not limited to:

  • characterization of systems, techniques and methods for motion and gait analysis
  • clinical reports using wearable sensors
  • wearable sensors, methods and/or techniques for physiological monitoring
  • wearable sensors, methods and/or techniques for medical decision making
  • wearable sensors, methods and/or techniques for telemedicine applications
  • wearable sensors, methods and/or techniques for activities modelling
  • wearable sensor for motion analysis
  • innovative applications of wearable sensor systems

Prof. Dr. Shigeru Tadano
Prof. Dr. Laura Gastaldi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Gait analysis
  • Motion analysis
  • Diagnostic tool
  • Health monitoring
  • Aged activity monitoring
  • Tele-rehabilitation

Published Papers (36 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Open AccessArticle
Influence of BMI on Gait Characteristics of Young Adults: 3D Evaluation Using Inertial Sensors
Sensors 2019, 19(19), 4221; https://doi.org/10.3390/s19194221 - 28 Sep 2019
Cited by 1
Abstract
Overweight/obesity is a physical condition that affects daily activities, including walking. The main purpose of this study was to identify if there is a relationship between body mass index (BMI) and gait characteristics in young adults. 12 normal weight (NW) and 10 overweight/obese [...] Read more.
Overweight/obesity is a physical condition that affects daily activities, including walking. The main purpose of this study was to identify if there is a relationship between body mass index (BMI) and gait characteristics in young adults. 12 normal weight (NW) and 10 overweight/obese (OW) individuals walked at a self-selected speed along a 14 m indoor path. H-Gait system, combining seven inertial sensors (fixed on pelvis and lower limbs), was used to record gait data. Walking speed, spatio-temporal parameters and joint kinematics in 3D were analyzed. Differences between NW and OW and correlations between BMI and gait parameters were evaluated. Conventional spatio-temporal parameters did not show statistical differences between the two groups or correlations with the BMI. However, significant results were pointed out for the joint kinematics. OW showed greater hip joint angles in frontal and transverse planes, with respect to NW. In the transverse plane, OW showed a greater knee opening angle and a shorter length of knee and ankle trajectories. Correlations were found between BMI and kinematic parameters in the frontal and transverse planes. Despite some phenomena such as soft tissue artifact and kinematics cross-talk, which have to be more deeply assessed, current results show a relationship between BMI and gait characteristics in young adults that should be looked at in osteoarthritis prevention. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements
Sensors 2019, 19(19), 4215; https://doi.org/10.3390/s19194215 - 28 Sep 2019
Cited by 5
Abstract
Tremor is one of the main symptoms of Parkinson’s Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does [...] Read more.
Tremor is one of the main symptoms of Parkinson’s Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients’ tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients’ tremor from continuous monitoring of the subjects’ movement in their natural environment. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessFeature PaperArticle
How to Select Balance Measures Sensitive to Parkinson’s Disease from Body-Worn Inertial Sensors—Separating the Trees from the Forest
Sensors 2019, 19(15), 3320; https://doi.org/10.3390/s19153320 - 28 Jul 2019
Cited by 7
Abstract
This study aimed to determine the most sensitive objective measures of balance dysfunction that differ between people with Parkinson’s Disease (PD) and healthy controls. One-hundred and forty-four people with PD and 79 age-matched healthy controls wore eight inertial sensors while performing tasks to [...] Read more.
This study aimed to determine the most sensitive objective measures of balance dysfunction that differ between people with Parkinson’s Disease (PD) and healthy controls. One-hundred and forty-four people with PD and 79 age-matched healthy controls wore eight inertial sensors while performing tasks to measure five domains of balance: standing posture (Sway), anticipatory postural adjustments (APAs), automatic postural responses (APRs), dynamic posture (Gait) and limits of stability (LOS). To reduce the initial 93 measures, we selected uncorrelated measures that were most sensitive to PD. After applying a threshold on the Standardized Mean Difference between PD and healthy controls, 44 measures remained; and after reducing highly correlated measures, 24 measures remained. The four most sensitive measures were from APAs and Gait domains. The random forest with 10-fold cross-validation on the remaining measures (n = 24) showed an accuracy to separate PD from healthy controls of 82.4%—identical to result for all measures. Measures from the most sensitive domains, APAs and Gait, were significantly correlated with the severity of disease and with patient-related outcomes. This method greatly reduced the objective measures of balance to the most sensitive for PD, while still capturing four of the five domains of balance. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Curve Similarity Model for Real-Time Gait Phase Detection Based on Ground Contact Forces
Sensors 2019, 19(14), 3235; https://doi.org/10.3390/s19143235 - 23 Jul 2019
Cited by 1
Abstract
This paper proposed a new novel method to adaptively detect gait patterns in real time through the ground contact forces (GCFs) measured by load cell. The curve similarity model (CSM) is used to identify the division of off-ground and on-ground statuses, and differentiate [...] Read more.
This paper proposed a new novel method to adaptively detect gait patterns in real time through the ground contact forces (GCFs) measured by load cell. The curve similarity model (CSM) is used to identify the division of off-ground and on-ground statuses, and differentiate gait patterns based on the detection rules. Traditionally, published threshold-based methods detect gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. In this paper, the curve is composed of a series of continuous or discrete ordered GCF data points, and the CSM is built offline to obtain a training template. Then, the testing curve is compared with the training template to figure out the degree of similarity. If the computed degree of similarity is less than a given threshold, they are considered to be similar, which would lead to the division of off-ground and on-ground statuses. Finally, gait patterns could be differentiated according to the status division based on the detection rules. In order to test the detection error rate of the proposed method, a method in the literature is introduced as the reference method to obtain comparative results. The experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively, and obtain a low error rate compared with the reference method. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Classifying Diverse Physical Activities Using “Smart Garments”
Sensors 2019, 19(14), 3133; https://doi.org/10.3390/s19143133 - 16 Jul 2019
Cited by 7
Abstract
Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. [...] Read more.
Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Estimation of Ankle Joint Power during Walking Using Two Inertial Sensors
Sensors 2019, 19(12), 2796; https://doi.org/10.3390/s19122796 - 21 Jun 2019
Cited by 4
Abstract
(1) Background: Ankle joint power, as an indicator of the ability to control lower limbs, is of great relevance for clinical diagnosis of gait impairment and control of lower limb prosthesis. However, the majority of available techniques for estimating joint power are based [...] Read more.
(1) Background: Ankle joint power, as an indicator of the ability to control lower limbs, is of great relevance for clinical diagnosis of gait impairment and control of lower limb prosthesis. However, the majority of available techniques for estimating joint power are based on inverse dynamics methods, which require performing a biomechanical analysis of the foot and using a highly instrumented environment to tune the parameters of the resulting biomechanical model. Such techniques are not generally applicable to real-world scenarios in which gait monitoring outside of the clinical setting is desired. This paper proposes a viable alternative to such techniques by using machine learning algorithms to estimate ankle joint power from data collected by two miniature inertial measurement units (IMUs) on the foot and shank, (2) Methods: Nine participants walked on a force-plate-instrumented treadmill wearing two IMUs. The data from the IMUs were processed to train and test a random forest model to estimate ankle joint power. The performance of the model was then evaluated by comparing the estimated power values to the reference values provided by the motion tracking system and the force-plate-instrumented treadmill. (3) Results: The proposed method achieved a high accuracy with the correlation coefficient, root mean square error, and normalized root mean square error of 0.98, 0.06 w/kg, and 1.05% in the intra-subject test, and 0.92, 0.13 w/kg, and 2.37% in inter-subject test, respectively. The difference between the predicted and true peak power values was 0.01 w/kg and 0.14 w/kg with a delay of 0.4% and 0.4% of gait cycle duration for the intra- and inter-subject testing, respectively. (4) Conclusions: The results of this study demonstrate the feasibility of using only two IMUs to estimate ankle joint power. The proposed technique provides a basis for developing a portable and compact gait monitoring system that can potentially offer monitoring and reporting on ankle joint power in real-time during activities of daily living. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Ski Position during the Flight and Landing Preparation Phases in Ski Jumping Detected with Inertial Sensors
Sensors 2019, 19(11), 2575; https://doi.org/10.3390/s19112575 - 06 Jun 2019
Abstract
Ski movement plays an important role during landing preparation, as well as in the whole ski jumping performance. Good landing preparation timing and correct ski position increase the jump length and reduce the impact forces. Inertial motion units (IMUs) placed on the skis [...] Read more.
Ski movement plays an important role during landing preparation, as well as in the whole ski jumping performance. Good landing preparation timing and correct ski position increase the jump length and reduce the impact forces. Inertial motion units (IMUs) placed on the skis could constitute a promising technology for analyzing the ski movements during training. During regular summer trainings, 10 elite athletes (17 ± 1 years) performed jumps while wearing IMUs and wireless force insoles. This set-up enabled the analysis of a possible correlation between ski movements and ground reaction force (GRF) during landing impact. The results showed that the pitch during the landing preparation is the most influential movement on the impact kinetic variables since it is related to the angle of attack, which affects the aerodynamics. The ski position at 0.16 s before landing did not influence the kinetics because the athlete was too close to the ground. During the impact, the roll angle did not correlate with GRF. Moreover, each athlete showed a different movement pattern during the flight phase. Concluding, the combination of IMUs and force insoles is a promising set-up to analyze ski jumping performance thanks to the fast placement, low weight, and high reliability. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Tracking Foot Drop Recovery Following Lumbar-Spine Surgery, Applying Multiclass Gait Classification Using Machine Learning Techniques
Sensors 2019, 19(11), 2542; https://doi.org/10.3390/s19112542 - 04 Jun 2019
Cited by 1
Abstract
The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. [...] Read more.
The ability to accurately perform human gait evaluation is critical for orthopedic foot and ankle surgeons in tracking the recovery process of their patients. The assessment of gait in an objective and accurate manner can lead to improvement in diagnoses, treatments, and recovery. Currently, visual inspection is the most common clinical method for evaluating the gait, but this method can be subjective and inaccurate. The aim of this study is to evaluate the foot drop condition in an accurate and clinically applicable manner. The gait data were collected from 56 patients suffering from foot drop with L5 origin gathered via a system based on inertial measurement unit sensors at different stages of surgical treatment. Various machine learning (ML) algorithms were applied to categorize the data into specific groups associated with the recovery stages. The results revealed that the random forest algorithm performed best out of the selected ML algorithms, with an overall 84.89% classification accuracy and 0.3785 mean absolute error for regression. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Real-Time Detection of Seven Phases of Gait in Children with Cerebral Palsy Using Two Gyroscopes
Sensors 2019, 19(11), 2517; https://doi.org/10.3390/s19112517 - 01 Jun 2019
Cited by 3
Abstract
A recently designed gait phase detection (GPD) system, with the ability to detect all seven phases of gait in healthy adults, was modified for GPD in children with cerebral palsy (CP). A shank-attached gyroscope sent angular velocity to a rule-based algorithm in LabVIEW [...] Read more.
A recently designed gait phase detection (GPD) system, with the ability to detect all seven phases of gait in healthy adults, was modified for GPD in children with cerebral palsy (CP). A shank-attached gyroscope sent angular velocity to a rule-based algorithm in LabVIEW to identify the distinct characteristics of the signal. Seven typically developing children (TD) and five children with CP were asked to walk on treadmill at their self-selected speed while using this system. Using only shank angular velocity, all seven phases of gait (Loading Response, Mid-Stance, Terminal Stance, Pre-Swing, Initial Swing, Mid-Swing and Terminal Swing) were reliably detected in real time. System performance was validated against two established GPD methods: (1) force-sensing resistors (GPD-FSR) (for typically developing children) and (2) motion capture (GPD-MoCap) (for both typically developing children and children with CP). The system detected over 99% of the phases identified by GPD-FSR and GPD-MoCap. Absolute values of average gait phase onset detection deviations relative to GPD-MoCap were less than 100 ms for both TD children and children with CP. The newly designed system, with minimized sensor setup and low processing burden, is cosmetic and economical, making it a viable solution for real-time stand-alone and portable applications such as triggering functional electrical stimulation (FES) in rehabilitation systems. This paper verifies the applicability of the GPD system to identify specific gait events for triggering FES to enhance gait in children with CP. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
New Considerations for Collecting Biomechanical Data Using Wearable Sensors: How Does Inclination Influence the Number of Runs Needed to Determine a Stable Running Gait Pattern?
Sensors 2019, 19(11), 2516; https://doi.org/10.3390/s19112516 - 01 Jun 2019
Cited by 2
Abstract
As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a ‘typical’ or ‘stable’ gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics [...] Read more.
As inertial measurement units (IMUs) are used to capture gait data in real-world environments, guidelines are required in order to determine a ‘typical’ or ‘stable’ gait pattern across multiple days of data collection. Since uphill and downhill running can greatly affect the biomechanics of running gait, this study sought to determine the number of runs needed to establish a stable running pattern during level, downhill, and uphill conditions for both univariate and multivariate analyses of running biomechanical data collected using a single wearable IMU device. Pelvic drop, ground contact time, braking, vertical oscillation, pelvic rotation, and cadence, were recorded from thirty-five recreational runners running in three elevation conditions: level, downhill, and uphill. Univariate and multivariate normal distributions were estimated from differing numbers of runs and stability was defined when the addition of a new run resulted in less than a 5% change in the 2.5 and 97.5 quantiles of the 95% probability density function for each individual runner. This stability point was determined separately for each runner and each IMU variable (univariate and multivariate). The results showed that 2–4 runs were needed to define a stable running pattern for univariate, and 4–5 days were necessary for multivariate analysis across all inclination conditions. Pearson’s correlation coefficients were calculated to cross-validate differing elevation conditions and showed excellent correlations (r = 0.98 to 1.0) comparing the training and testing data within the same elevation condition and good to very good correlations (r = 0.63–0.88) when comparing training and testing data from differing elevation conditions. These results suggest that future research involving wearable technology should collect multiple days of data in order to build reliable and accurate representations of an individual’s stable gait pattern. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Open AccessArticle
Evaluation of Gait Phase Detection Delay Compensation Strategies to Control a Gyroscope-Controlled Functional Electrical Stimulation System During Walking
Sensors 2019, 19(11), 2471; https://doi.org/10.3390/s19112471 - 30 May 2019
Cited by 6
Abstract
Functional electrical stimulation systems are used as neuroprosthetic devices in rehabilitative interventions such as gait training. Stimulator triggers, implemented to control stimulation delivery, range from open- to closed-loop controllers. Finite-state controllers trigger stimulators when specific conditions are met and utilize preset sequences of [...] Read more.
Functional electrical stimulation systems are used as neuroprosthetic devices in rehabilitative interventions such as gait training. Stimulator triggers, implemented to control stimulation delivery, range from open- to closed-loop controllers. Finite-state controllers trigger stimulators when specific conditions are met and utilize preset sequences of stimulation. Wearable sensors provide the necessary input to differentiate gait phases during walking and trigger stimulation. However, gait phase detection is associated with inherent system delays. In this study, five stimulator triggers designed to compensate for gait phase detection delays were tested to determine which trigger most accurately delivered stimulation at the desired times of the gait cycle. Motion capture data were collected on seven typically-developing children while walking on an instrumented treadmill. Participants wore one inertial measurement unit on each ankle and gyroscope data were streamed into the gait phase detection algorithm. Five triggers, based on gait phase detection, were used to simulate stimulation to five muscle groups, bilaterally. For each condition, stimulation signals were collected in the motion capture software via analog channels and compared to the desired timing determined by kinematic and kinetic data. Results illustrate that gait phase detection is a viable finite-state control, and appropriate system delay compensations, on average, reduce stimulation delivery delays by 6.7% of the gait cycle. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
An Investigation on the Sampling Frequency of the Upper-Limb Force Myographic Signals
Sensors 2019, 19(11), 2432; https://doi.org/10.3390/s19112432 - 28 May 2019
Cited by 4
Abstract
Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human–machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of [...] Read more.
Force myography (FMG) is an emerging method to register muscle activity of a limb using force sensors for human–machine interface and movement monitoring applications. Despite its newly gained popularity among researchers, many of its fundamental characteristics remain to be investigated. The aim of this study is to identify the minimum sampling frequency needed for recording upper-limb FMG signals without sacrificing signal integrity. Twelve healthy volunteers participated in an experiment in which they were instructed to perform rapid hand actions with FMG signals being recorded from the wrist and the bulk region of the forearm. The FMG signals were sampled at 1 kHz with a 16-bit resolution data acquisition device. We downsampled the signals with frequencies ranging from 1 Hz to 500 Hz to examine the discrepancies between the original signals and the downsampled ones. Based on the results, we suggest that FMG signals from the forearm and wrist should be collected with minimum sampling frequencies of 54 Hz and 58 Hz for deciphering isometric actions, and 70 Hz and 84 Hz for deciphering dynamic actions. This fundamental work provides insight into minimum requirements for sampling FMG signals such that the data content of such signals is not compromised. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Wearable Embedded Intelligence for Detection of Falls Independently of on-Body Location
Sensors 2019, 19(11), 2426; https://doi.org/10.3390/s19112426 - 28 May 2019
Cited by 4
Abstract
Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and [...] Read more.
Falls are one of the most common problems in the elderly population. Therefore, each year more solutions for automatic fall detection are emerging. This paper proposes a single accelerometer algorithm for wearable devices that works for three different body locations: chest, waist and pocket, without a calibration step being required. This algorithm is able to be fully executed on a wearable device and no external devices are necessary for data processing. Additionally, a study of the accelerometer sampling rate, that allows the algorithm to achieve a better performance, was performed. The algorithm was validated with a continuous dataset with daily living activities and 272 simulated falls. Considering the trade-off between sensitivity and the number of false alarms the most suitable sampling rate found was 50 Hz. The proposed algorithm was able to achieve a trade-off of no false alarms and 89.5% of fall detection rate when wearing the sensor on the user’s waist with a medium sensitivity level of the algorithm. In conclusion, this paper presents a reliable solution for automatic fall detection that can be adapted to different usages and conditions, since it can be used in different body locations and its sensitivity can be adapted to different subjects according to their physical activity level. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
The Validity of a Mixed Reality-Based Automated Functional Mobility Assessment
Sensors 2019, 19(9), 2183; https://doi.org/10.3390/s19092183 - 11 May 2019
Cited by 4
Abstract
Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in older adults. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLensTM headset to automatically lead [...] Read more.
Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in older adults. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLensTM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older and 12 young adults) participated in this study. An automated functional mobility assessment app was developed, based on the HoloLens platform. The mobility performance was recorded with the headset built-in sensor and reference inertial sensor (Opal, APDM) taped on the headset and lower back. The results indicate that the vertical kinematic measurements by HoloLens were in good agreement with the reference sensor (Normalized RMSE ~ 10%, except for cases where the inertial sensor drift correction was not viable). Additionally, the HoloLens-based test completion time was in perfect agreement with the clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users (without severe mobility deficit) to complete common mobility tests, and this approach has the potential to provide an objective and efficient sensor-based mobility assessment that does not require any direct research/clinical oversight. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Symmetry of Gait in Underweight, Normal and Overweight Children and Adolescents
Sensors 2019, 19(9), 2054; https://doi.org/10.3390/s19092054 - 02 May 2019
Cited by 3
Abstract
Abnormal excess or lack of body mass can influence gait patterns, but in some cases such differences are subtle and not easy to detect, even with quantitative techniques for movement analysis. In these situations, the study of trunk accelerations may represent an effective [...] Read more.
Abnormal excess or lack of body mass can influence gait patterns, but in some cases such differences are subtle and not easy to detect, even with quantitative techniques for movement analysis. In these situations, the study of trunk accelerations may represent an effective way to detecting gait anomalies in terms of symmetry through the calculation of Harmonic Ratio (HR), a parameter obtained by processing trunk accelerations in the frequency domain. In the present study we used this technique to assess the existence of differences in HR during gait in a cohort of 75 healthy children and early adolescents (aged 7–14 years) stratified into 3 equally-sized age and gender-matched groups (Underweight: UW; Normal Weight: NW; Overweight: OW). The accelerometric signal, acquired using a single wearable inertial sensor, was processed to calculate stride length, speed, cadence and HR in antero-posterior, vertical and medio-lateral directions. No differences in spatio-temporal parameters were found among groups, while the HR in the medio-lateral direction was found significantly lower in UW children, while OW exhibited the highest values. On the basis of the results obtained, HR appears capable of discriminating gait symmetry in children with different body mass even when conventional gait parameters are unchanged. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Ground Reaction Forces and Kinematics of Ski Jump Landing Using Wearable Sensors
Sensors 2019, 19(9), 2011; https://doi.org/10.3390/s19092011 - 29 Apr 2019
Cited by 6
Abstract
In the past, technological issues limited research focused on ski jump landing. Today, thanks to the development of wearable sensors, it is possible to analyze the biomechanics of athletes without interfering with their movements. The aims of this study were twofold. Firstly, the [...] Read more.
In the past, technological issues limited research focused on ski jump landing. Today, thanks to the development of wearable sensors, it is possible to analyze the biomechanics of athletes without interfering with their movements. The aims of this study were twofold. Firstly, the quantification of the kinetic magnitude during landing is performed using wireless force insoles while 22 athletes jumped during summer training on the hill. In the second part, the insoles were combined with inertial motion units (IMUs) to determine the possible correlation between kinematics and kinetics during landing. The maximal normal ground reaction force (GRFmax) ranged between 1.1 and 5.3 body weight per foot independently when landing using the telemark or parallel leg technique. The GRFmax and impulse were correlated with flying time (p < 0.001). The hip flexions/extensions and the knee and hip rotations of the telemark front leg correlated with GRFmax (r = 0.689, p = 0.040; r = −0.670, p = 0.048; r = 0.820, p = 0.007; respectively). The force insoles and their combination with IMUs resulted in promising setups to analyze landing biomechanics and to provide in-field feedback to the athletes, being quick to place and light, without limiting movement. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Sock-Type Wearable Sensor for Estimating Lower Leg Muscle Activity Using Distal EMG Signals
Sensors 2019, 19(8), 1954; https://doi.org/10.3390/s19081954 - 25 Apr 2019
Cited by 3
Abstract
Lower leg muscle activity contributes to body control; thus, monitoring lower leg muscle activity is beneficial to understand the body condition and prevent accidents such as falls. Amplitude features such as the mean absolute values of electromyography (EMG) are used widely for monitoring [...] Read more.
Lower leg muscle activity contributes to body control; thus, monitoring lower leg muscle activity is beneficial to understand the body condition and prevent accidents such as falls. Amplitude features such as the mean absolute values of electromyography (EMG) are used widely for monitoring muscle activity. Garment-type EMG measurement systems use electrodes and they enable us to monitor muscle activity in daily life without any specific knowledge and the installation for electrode placement. However, garment-type measurement systems require a high compression area around the electrodes to prevent electrode displacement. This makes it difficult for users to wear such measurement systems. A less restraining wearable system, wherein the electrodes are placed around the ankle, is realized for target muscles widely distributed around the shank. The signals obtained from around the ankle are propagated biosignals from several muscles, and are referred to as distal EMG signals. Our objective is to develop a sock-type wearable sensor for estimating lower leg muscle activity using distal EMG signals. We propose a signal processing method based on multiple bandpass filters from the perspectives of noise separation and feature augmentation. We conducted an experiment for designing the hardware configuration, and three other experiments for evaluating the estimation accuracy and dependability of muscle activity analysis. Compared to the baseline based on a 20-500 Hz bandpass filter, the results indicated that the proposed system estimates muscle activity with higher accuracy. Experimental results suggest that lower leg muscle activity can be estimated using distal EMG signals. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Validity of Wearable Sensors at the Shoulder Joint: Combining Wireless Electromyography Sensors and Inertial Measurement Units to Perform Physical Workplace Assessments
Sensors 2019, 19(8), 1885; https://doi.org/10.3390/s19081885 - 20 Apr 2019
Cited by 4
Abstract
Background: Workplace adaptation is the preferred method of intervention to diminish risk factors associated with the development of work-related shoulder disorders. However, the majority of the workplace assessments performed are subjective (e.g., questionnaires). Quantitative assessments are required to support workplace adaptations. The [...] Read more.
Background: Workplace adaptation is the preferred method of intervention to diminish risk factors associated with the development of work-related shoulder disorders. However, the majority of the workplace assessments performed are subjective (e.g., questionnaires). Quantitative assessments are required to support workplace adaptations. The aims of this study are to assess the concurrent validity of inertial measurement units (IMUs; MVN, Xsens) in comparison to a motion capture system (Vicon) during lifting tasks, and establish the discriminative validity of a wireless electromyography (EMG) system for the evaluation of muscle activity. Methods: Sixteen participants performed 12 simple tasks (shoulder flexion, abduction, scaption) and 16 complex lifting tasks (lifting crates of different weights at different heights). A Delsys Trigno EMG system was used to record anterior and middle deltoids’ EMG activity, while the Xsens and Vicon simultaneously recorded shoulder kinematics. Results: For IMUs, correlation coefficients were high (simple task: >0.968; complex task: >0.84) and RMSEs were low (simple task: <6.72°; complex task: <11.5°). For EMG, a significant effect of weight, height and a weight x height interaction (anterior: p < 0.001; middle: p < 0.03) were observed for RMS EMG activity. Conclusions: These results suggest that wireless EMG and IMUs are valid units that can be used to measure physical demand in workplace assessments. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds
Sensors 2019, 19(8), 1869; https://doi.org/10.3390/s19081869 - 19 Apr 2019
Cited by 6
Abstract
The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the [...] Read more.
The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects’ step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Use of a Single Wireless IMU for the Segmentation and Automatic Analysis of Activities Performed in the 3-m Timed Up & Go Test
Sensors 2019, 19(7), 1647; https://doi.org/10.3390/s19071647 - 06 Apr 2019
Cited by 1
Abstract
Falls represent a major public health problem in the elderly population. The Timed Up & Go test (TU & Go) is the most used tool to measure this risk of falling, which offers a unique parameter in seconds that represents the dynamic balance. [...] Read more.
Falls represent a major public health problem in the elderly population. The Timed Up & Go test (TU & Go) is the most used tool to measure this risk of falling, which offers a unique parameter in seconds that represents the dynamic balance. However, it is not determined in which activity the subject presents greater difficulties. For this, a feature-based segmentation method using a single wireless Inertial Measurement Unit (IMU) is proposed in order to analyze data of the inertial sensors to provide a complete report on risks of falls. Twenty-five young subjects and 12 older adults were measured to validate the method proposed with an IMU in the back and with video recording. The measurement system showed similar data compared to the conventional test video recorded, with a Pearson correlation coefficient of 0.9884 and a mean error of 0.17 ± 0.13 s for young subjects, as well as a correlation coefficient of 0.9878 and a mean error of 0.2 ± 0.22 s for older adults. Our methodology allows for identifying all the TU & Go sub–tasks with a single IMU automatically providing information about variables such as: duration of sub–tasks, standing and sitting accelerations, rotation velocity of turning, number of steps during walking and turns, and the inclination degrees of the trunk during standing and sitting. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions
Sensors 2019, 19(7), 1483; https://doi.org/10.3390/s19071483 - 27 Mar 2019
Cited by 5
Abstract
The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this [...] Read more.
The identification of the initial contact (IC) and toe off (TO) events are crucial components of running gait analyses. To evaluate running gait in real-world settings, robust gait event detection algorithms that are based on signals from wearable sensors are needed. In this study, algorithms for identifying gait events were developed for accelerometers that were placed on the foot and low back and validated against a gold standard force plate gait event detection method. These algorithms were automated to enable the processing of large quantities of data by accommodating variability in running patterns. An evaluation of the accuracy of the algorithms was done by comparing the magnitude and variability of the difference between the back and foot methods in different running conditions, including different speeds, foot strike patterns, and outdoor running surfaces. The results show the magnitude and variability of the back-foot difference was consistent across running conditions, suggesting that the gait event detection algorithms can be used in a variety of settings. As wearable technology allows for running gait analyses to move outside of the laboratory, the use of automated accelerometer-based gait event detection methods may be helpful in the real-time evaluation of running patterns in real world conditions. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Continuous Analysis of Running Mechanics by Means of an Integrated INS/GPS Device
Sensors 2019, 19(6), 1480; https://doi.org/10.3390/s19061480 - 26 Mar 2019
Cited by 2
Abstract
This paper describes a single body-mounted sensor that integrates accelerometers, gyroscopes, compasses, barometers, a GPS receiver, and a methodology to process the data for biomechanical studies. The sensor and its data processing system can accurately compute the speed, acceleration, angular velocity, and angular [...] Read more.
This paper describes a single body-mounted sensor that integrates accelerometers, gyroscopes, compasses, barometers, a GPS receiver, and a methodology to process the data for biomechanical studies. The sensor and its data processing system can accurately compute the speed, acceleration, angular velocity, and angular orientation at an output rate of 400 Hz and has the ability to collect large volumes of ecologically-valid data. The system also segments steps and computes metrics for each step. We analyzed the sensitivity of these metrics to changing the start time of the gait cycle. Along with traditional metrics, such as cadence, speed, step length, and vertical oscillation, this system estimates ground contact time and ground reaction forces using machine learning techniques. This equipment is less expensive and cumbersome than the currently used alternatives: Optical tracking systems, in-shoe pressure measurement systems, and force plates. Another advantage, compared to existing methods, is that natural movement is not impeded at the expense of measurement accuracy. The proposed technology could be applied to different sports and activities, including walking, running, motion disorder diagnosis, and geriatric studies. In this paper, we present the results of tests in which the system performed real-time estimation of some parameters of walking and running which are relevant to biomechanical research. Contact time and ground reaction forces computed by the neural network were found to be as accurate as those obtained by an in-shoe pressure measurement system. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Measurement of the Chair Rise Performance of Older People Based on Force Plates and IMUs
Sensors 2019, 19(6), 1370; https://doi.org/10.3390/s19061370 - 19 Mar 2019
Cited by 5
Abstract
An early detection of functional decline with age is important to start interventions at an early state and to prolong the functional fitness. In order to assure such an early detection, functional assessments must be conducted on a frequent and regular basis. Since [...] Read more.
An early detection of functional decline with age is important to start interventions at an early state and to prolong the functional fitness. In order to assure such an early detection, functional assessments must be conducted on a frequent and regular basis. Since the five time chair rise test (5CRT) is a well-established test in the geriatric field, this test should be supported by technology. We introduce an approach that automatically detects the execution of the chair rise test via an inertial sensor integrated into a belt. The system’s suitability was evaluated via 20 subjects aged 72–89 years (78.2 ± 4.6 years) and was measured by a stopwatch, the inertial measurement unit (IMU), a Kinect® camera and a force plate. A Multilayer Perceptrons-based classifier detects transitions in the IMU data with an F1-Score of around 94.8%. Valid executions of the 5CRT are detected based on the correct occurrence of sequential movements via a rule-based model. The results of the automatically calculated test durations are in good agreement with the stopwatch measurements (correlation coefficient r = 0.93 (p < 0.001)). The analysis of the duration of single test cycles indicates a beginning fatigue at the end of the test. The comparison of the movement pattern within one person shows similar movement patterns, which differ only slightly in form and duration, whereby different subjects indicate variations regarding their performance strategies. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
A Multi-Path Compensation Method for Ranging in Wearable Ultrasonic Sensor Networks for Human Gait Analysis
Sensors 2019, 19(6), 1350; https://doi.org/10.3390/s19061350 - 18 Mar 2019
Abstract
Gait analysis in unrestrained environments can be done with a single wearable ultrasonic sensor node on the lower limb and four fixed anchor nodes. The accuracy demanded by such systems is very high. Chirp signals can provide better ranging and localization performance in [...] Read more.
Gait analysis in unrestrained environments can be done with a single wearable ultrasonic sensor node on the lower limb and four fixed anchor nodes. The accuracy demanded by such systems is very high. Chirp signals can provide better ranging and localization performance in ultrasonic systems. However, we cannot neglect the multi-path effect in typical indoor environments for ultrasonic signals. The multi-path components closer to the line of sight component cannot be identified during correlation reception which leads to errors in the estimated range and which in turn affects the localization and tracking performance. We propose a novel method to reduce the multi-path effect in ultrasonic sensor networks in typical indoor environments. A gait analysis system with one mobile node attached to the lower limb was designed to test the performance of the proposed system during an indoor treadmill walking experiment. An optical motion capture system was used as a benchmark for the experiments. The proposed method gave better tracking accuracy compared to conventional coherent receivers. The static measurements gave 2.45 mm standard deviation compared to 10.45 mm using the classical approach. The RMSE between the ultrasonic gait analysis system and the reference system improved from 28.70 mm to 22.28 mm. The gait analysis system gave good performance for extraction of spatial and temporal parameters. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Wearable Sensors System for an Improved Analysis of Freezing of Gait in Parkinson’s Disease Using Electromyography and Inertial Signals
Sensors 2019, 19(4), 948; https://doi.org/10.3390/s19040948 - 23 Feb 2019
Cited by 14
Abstract
We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology [...] Read more.
We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson’s disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
A Magnet-Based Timing System to Detect Gate Crossings in Alpine Ski Racing
Sensors 2019, 19(4), 940; https://doi.org/10.3390/s19040940 - 22 Feb 2019
Cited by 2
Abstract
In alpine skiing, intermediate times are usually measured with photocells. However, for practical reasons, the number of intermediate cells is limited to three–four, making a detailed timing analysis difficult. In this paper, we propose and validate a magnet-based timing system allowing for the [...] Read more.
In alpine skiing, intermediate times are usually measured with photocells. However, for practical reasons, the number of intermediate cells is limited to three–four, making a detailed timing analysis difficult. In this paper, we propose and validate a magnet-based timing system allowing for the measurement of intermediate times at each gate. Specially designed magnets were placed at each gate and the athletes wore small magnetometers on their lower back to measure the instantaneous magnetic field. The athlete’s gate crossings caused peaks in the measured signal which could then be related to the precise instants of gate crossings. The system was validated against photocells placed at four gates of a slalom skiing course. Eight athletes skied the course twice and one run per athlete was included in the validation study. The 95% error intervals for gate-to-gate timing and section times were below 0.025 s. Each athlete’s gate-to-gate times were compared to the group’s average gate-to-gate times, revealing small performance differences that would otherwise be difficult to measure with a traditional photocell-based system. The system could be used to identify the effect of tactical choices and athlete specific skiing skills on performance and could allow a more efficient and athlete-specific performance analysis and feedback. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
The Use of Wearable Sensors for the Movement Assessment on Muscle Contraction Sequences in Post-Stroke Patients during Sit-to-Stand
Sensors 2019, 19(3), 657; https://doi.org/10.3390/s19030657 - 06 Feb 2019
Cited by 1
Abstract
Electromyography (EMG) sensors have been used to study the sequence of muscle contractions during sit-to-stand (STS) in post-stroke patients. However, the majority of the studies used wired sensors with a limited number of placements. Using the latest improved wearable technology with 16 sensors, [...] Read more.
Electromyography (EMG) sensors have been used to study the sequence of muscle contractions during sit-to-stand (STS) in post-stroke patients. However, the majority of the studies used wired sensors with a limited number of placements. Using the latest improved wearable technology with 16 sensors, the current study was a thorough investigation to evaluate the contraction sequences of eight key muscles on the trunk and bilateral limbs during STS in post-stroke patients, as it became feasible. Multiple wearable sensors for the detection of muscle contraction sequences showed that the post-stroke patients performed STS with abnormal firing sequences, not only in the primary mover on the sagittal plane during raising, but also in the tibialis anterior, which may affect anticipatory postural adjustment in the gluteus medius, which may affect balance control. The abnormal tibialis anterior contraction until the early ascending phase and the delayed firing of the gluteus muscles highlight the importance of whole-kinetic-chain monitoring of contraction sequences using wearable sensors. The findings can be helpful for the design of therapeutic exercises. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Wearable Sensor Based Stooped Posture Estimation in Simulated Parkinson’s Disease Gaits
Sensors 2019, 19(2), 223; https://doi.org/10.3390/s19020223 - 09 Jan 2019
Cited by 3
Abstract
Stooping is a posture which is described as an involuntary forward bending of the thoracolumbar spine. Conventionally, the stooped posture (SP) in Parkinson’s disease patients is measured in static or limited movement conditions using a radiological or optoelectronic system. In the dynamic condition [...] Read more.
Stooping is a posture which is described as an involuntary forward bending of the thoracolumbar spine. Conventionally, the stooped posture (SP) in Parkinson’s disease patients is measured in static or limited movement conditions using a radiological or optoelectronic system. In the dynamic condition with long movement distance, there was no effective method in preference to the empirical assessment from doctors. In this research, we proposed a practical method for estimating the SP with a high accuracy where accelerometers can be mounted on the neck or upper back as a wearable sensor. The experiments with simulated subjects showed a high correlation of 0.96 and 0.99 between the estimated SP angle and the reference angles for neck and back sensor position, respectively. The maximum absolute error (0.9 and 1.5 degrees) indicated that the system can be used, not only in clinical assessment as a measurement, but also in daily life as a corrector. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Towards Wearable Comprehensive Capture and Analysis of Skeletal Muscle Activity during Human Locomotion
Sensors 2019, 19(1), 195; https://doi.org/10.3390/s19010195 - 07 Jan 2019
Cited by 6
Abstract
Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. [...] Read more.
Background: Motion capture and analyzing systems are essential for understanding locomotion. However, the existing devices are too cumbersome and can be used indoors only. A newly-developed wearable motion capture and measurement system with multiple sensors and ultrasound imaging was introduced in this study. Methods: In ten healthy participants, the changes in muscle area and activity of gastrocnemius, plantarflexion and dorsiflexion of right leg during walking were evaluated by the developed system and the Vicon system. The existence of significant changes in a gait cycle, comparison of the ankle kinetic data captured by the developed system and the Vicon system, and test-retest reliability (evaluated by the intraclass correlation coefficient, ICC) in each channel’s data captured by the developed system were examined. Results: Moderate to good test-retest reliability of various channels of the developed system (0.512 ≤ ICC ≤ 0.988, p < 0.05), significantly high correlation between the developed system and Vicon system in ankle joint angles (0.638R ≤ 0.707, p < 0.05), and significant changes in muscle activity of gastrocnemius during a gait cycle (p < 0.05) were found. Conclusion: A newly developed wearable motion capture and measurement system with ultrasound imaging that can accurately capture the motion of one leg was evaluated in this study, which paves the way towards real-time comprehensive evaluation of muscles and joint motions during different activities in both indoor and outdoor environments. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering from Double Trans-Femoral Amputation
Sensors 2018, 18(12), 4146; https://doi.org/10.3390/s18124146 - 26 Nov 2018
Cited by 3
Abstract
Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the legs, [...] Read more.
Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the legs, which can be applied in prosthesis to imitate the missing part of the leg in walking. In particular, we propose a method, called GaIn, to control non-invasive, robotic, prosthetic legs. GaIn can infer the movements of both missing shanks and feet for humans suffering from double trans-femoral amputation using biologically inspired recurrent neural networks. Predictions are performed for casual walking related activities such as walking, taking stairs, and running based on thigh movement. In our experimental tests, GaIn achieved a 4.55° prediction error for shank movements on average. However, a patient’s intention to stand up and sit down cannot be inferred from thigh movements. In fact, intention causes thigh movements while the shanks and feet remain roughly still. The GaIn system can be triggered by thigh muscle activities measured with electromyography (EMG) sensors to make robotic prosthetic legs perform standing up and sitting down actions. The GaIn system has low prediction latency and is fast and computationally inexpensive to be deployed on mobile platforms and portable devices. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessFeature PaperArticle
Smart Shoe-Assisted Evaluation of Using a Single Trunk/Pocket-Worn Accelerometer to Detect Gait Phases
Sensors 2018, 18(11), 3811; https://doi.org/10.3390/s18113811 - 07 Nov 2018
Cited by 6
Abstract
Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing [...] Read more.
Wearable sensors may enable the continuous monitoring of gait out of the clinic without requiring supervised tests and costly equipment. This paper investigates the use of a single wearable accelerometer to detect foot contact times and estimate temporal gait parameters (stride time, swing and stance duration). The experiments considered two possible body positions for the accelerometer: over the lower trunk and inside a trouser pocket. The latter approach could be implemented using a common smartphone. Notably, during the experiments, the ground truth was obtained by using a pair of sensorized shoes. Unlike ambient sensors and camera-based systems, sensorized shoes enable the evaluation of body-worn sensors even during longer walks. Experiments showed that both trunk and pocket positions achieved promising results in estimating gait parameters, with a mean absolute error below 50 ms. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Adjustable Method for Real-Time Gait Pattern Detection Based on Ground Reaction Forces Using Force Sensitive Resistors and Statistical Analysis of Constant False Alarm Rate
Sensors 2018, 18(11), 3764; https://doi.org/10.3390/s18113764 - 03 Nov 2018
Cited by 1
Abstract
A new approach is proposed to detect the real-time gait patterns adaptively through measuring the ground contact forces (GCFs) by force sensitive resistors (FSRs). Published threshold-based methods detect the gait patterns by means of setting a fixed threshold to divide the GCFs into [...] Read more.
A new approach is proposed to detect the real-time gait patterns adaptively through measuring the ground contact forces (GCFs) by force sensitive resistors (FSRs). Published threshold-based methods detect the gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. To overcome these drawbacks, this study utilized the constant false alarm rate (CFAR) to analyze the characteristics of GCF signals. Specifically, a sliding window detector is built to record the lasting time of the curvature of the GCF signals and one complete gait cycle could be divided into three areas, such as continuous ascending area, continuous descending area and unstable area. Then, the GCF values in the unstable area are used to compute a threshold through the CFAR. Finally, the new gait pattern detection rules are proposed which include the results of the sliding window detector and the division results through the computed threshold. To verify this idea, a data acquisition board is designed to collect the GCF data from able-bodied subjects. Meanwhile, in order to test the reliability of the proposed method, five threshold-based methods in the literature are introduced as reference methods and the reliability is validated by comparing the detection results of the proposed method with those of the reference methods. Experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively and obtain high reliabilities compared with the reference methods. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessArticle
Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson’s Disease and Autism Spectrum Disorders
Sensors 2018, 18(10), 3533; https://doi.org/10.3390/s18103533 - 19 Oct 2018
Cited by 11
Abstract
Detecting and monitoring of abnormal movement behaviors in patients with Parkinson’s Disease (PD) and individuals with Autism Spectrum Disorders (ASD) are beneficial for adjusting care and medical treatment in order to improve the patient’s quality of life. Supervised methods commonly used in the [...] Read more.
Detecting and monitoring of abnormal movement behaviors in patients with Parkinson’s Disease (PD) and individuals with Autism Spectrum Disorders (ASD) are beneficial for adjusting care and medical treatment in order to improve the patient’s quality of life. Supervised methods commonly used in the literature need annotation of data, which is a time-consuming and costly process. In this paper, we propose deep normative modeling as a probabilistic novelty detection method, in which we model the distribution of normal human movements recorded by wearable sensors and try to detect abnormal movements in patients with PD and ASD in a novelty detection framework. In the proposed deep normative model, a movement disorder behavior is treated as an extreme of the normal range or, equivalently, as a deviation from the normal movements. Our experiments on three benchmark datasets indicate the effectiveness of the proposed method, which outperforms one-class SVM and the reconstruction-based novelty detection approaches. Our contribution opens the door toward modeling normal human movements during daily activities using wearable sensors and eventually real-time abnormal movement detection in neuro-developmental and neuro-degenerative disorders. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Graphical abstract

Open AccessArticle
Peripheral Network Connectivity Analyses for the Real-Time Tracking of Coupled Bodies in Motion
Sensors 2018, 18(9), 3117; https://doi.org/10.3390/s18093117 - 15 Sep 2018
Cited by 4
Abstract
Dyadic interactions are ubiquitous in our lives, yet they are highly challenging to study. Many subtle aspects of coupled bodily dynamics continuously unfolding during such exchanges have not been empirically parameterized. As such, we have no formal statistical methods to describe the spontaneously [...] Read more.
Dyadic interactions are ubiquitous in our lives, yet they are highly challenging to study. Many subtle aspects of coupled bodily dynamics continuously unfolding during such exchanges have not been empirically parameterized. As such, we have no formal statistical methods to describe the spontaneously self-emerging coordinating synergies within each actor’s body and across the dyad. Such cohesive motion patterns self-emerge and dissolve largely beneath the awareness of the actors and the observers. Consequently, hand coding methods may miss latent aspects of the phenomena. The present paper addresses this gap and provides new methods to quantify the moment-by-moment evolution of self-emerging cohesiveness during highly complex ballet routines. We use weighted directed graphs to represent the dyads as dynamically coupled networks unfolding in real-time, with activities captured by a grid of wearable sensors distributed across the dancers’ bodies. We introduce new visualization tools, signal parameterizations, and a statistical platform that integrates connectivity metrics with stochastic analyses to automatically detect coordination patterns and self-emerging cohesive coupling as they unfold in real-time. Potential applications of these new techniques are discussed in the context of personalized medicine, basic research, and the performing arts. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Graphical abstract

Review

Jump to: Research

Open AccessFeature PaperReview
Wearable Inertial Sensors to Assess Standing Balance: A Systematic Review
Sensors 2019, 19(19), 4075; https://doi.org/10.3390/s19194075 - 20 Sep 2019
Cited by 22
Abstract
Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. [...] Read more.
Wearable sensors are de facto revolutionizing the assessment of standing balance. The aim of this work is to review the state-of-the-art literature that adopts this new posturographic paradigm, i.e., to analyse human postural sway through inertial sensors directly worn on the subject body. After a systematic search on PubMed and Scopus databases, two raters evaluated the quality of 73 full-text articles, selecting 47 high-quality contributions. A good inter-rater reliability was obtained (Cohen’s kappa = 0.79). This selection of papers was used to summarize the available knowledge on the types of sensors used and their positioning, the data acquisition protocols and the main applications in this field (e.g., “active aging”, biofeedback-based rehabilitation for fall prevention, and the management of Parkinson’s disease and other balance-related pathologies), as well as the most adopted outcome measures. A critical discussion on the validation of wearable systems against gold standards is also presented. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Open AccessEditor’s ChoiceReview
Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review
Sensors 2019, 19(7), 1555; https://doi.org/10.3390/s19071555 - 31 Mar 2019
Cited by 25
Abstract
Motion capture systems are recognized as the gold standard for joint angle calculation. However, studies using these systems are restricted to laboratory settings for technical reasons, which may lead to findings that are not representative of real-life context. Recently developed commercial and home-made [...] Read more.
Motion capture systems are recognized as the gold standard for joint angle calculation. However, studies using these systems are restricted to laboratory settings for technical reasons, which may lead to findings that are not representative of real-life context. Recently developed commercial and home-made inertial measurement sensors (M/IMU) are potentially good alternatives to the laboratory-based systems, and recent technology improvements required a synthesis of the current evidence. The aim of this systematic review was to determine the criterion validity and reliability of M/IMU for each body joint and for tasks of different levels of complexity. Five different databases were screened (Pubmed, Cinhal, Embase, Ergonomic abstract, and Compendex). Two evaluators performed independent selection, quality assessment (consensus-based standards for the selection of health measurement instruments [COSMIN] and quality appraisal tools), and data extraction. Forty-two studies were included. Reported validity varied according to task complexity (higher validity for simple tasks) and the joint evaluated (better validity for lower limb joints). More studies on reliability are needed to make stronger conclusions, as the number of studies addressing this psychometric property was limited. M/IMU should be considered as a valid tool to assess whole body range of motion, but further studies are needed to standardize technical procedures to obtain more accurate data. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait and Motion Analysis 2018)
Show Figures

Figure 1

Back to TopTop