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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: 25 June 2019

Special Issue Editors

Guest Editor
Prof. Dr. Shigeru Tadano

National Institute of Technology, Hakodate College, Hakodatate, Japan and Hokkaido University, Sapporo, Japan
E-Mail
Fax: +81 11 706 6405
Interests: biomechanical engineering; musculo-skeletal and orthopaedic biomechanics; bone mechanics; medical and healthcare engineering
Guest Editor
Prof. Dr. Laura Gastaldi

Department of Mathematical Sciences, Politecnico di Torino, Torino, Italy
Website | E-Mail
Interests: biomechanical engineering; gait analysis; motion analysis; adaptive sports; Rehabilitation; medical and healthcare engineering

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

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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 1800 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 (16 papers)

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Research

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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
Received: 15 February 2019 / Revised: 12 March 2019 / Accepted: 19 March 2019 / Published: 6 April 2019
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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)
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Open AccessArticle Automated Accelerometer-Based Gait Event Detection During Multiple Running Conditions
Sensors 2019, 19(7), 1483; https://doi.org/10.3390/s19071483
Received: 7 February 2019 / Revised: 15 March 2019 / Accepted: 22 March 2019 / Published: 27 March 2019
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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)
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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
Received: 12 February 2019 / Revised: 11 March 2019 / Accepted: 21 March 2019 / Published: 26 March 2019
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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)
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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
Received: 25 January 2019 / Revised: 27 February 2019 / Accepted: 15 March 2019 / Published: 19 March 2019
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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)
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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
Received: 15 February 2019 / Revised: 7 March 2019 / Accepted: 14 March 2019 / Published: 18 March 2019
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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)
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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
Received: 15 January 2019 / Revised: 19 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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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)
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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
Received: 28 January 2019 / Revised: 20 February 2019 / Accepted: 20 February 2019 / Published: 22 February 2019
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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)
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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
Received: 25 October 2018 / Revised: 14 January 2019 / Accepted: 16 January 2019 / Published: 6 February 2019
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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)
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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
Received: 6 December 2018 / Revised: 3 January 2019 / Accepted: 5 January 2019 / Published: 9 January 2019
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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)
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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
Received: 29 November 2018 / Revised: 22 December 2018 / Accepted: 4 January 2019 / Published: 7 January 2019
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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)
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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
Received: 24 October 2018 / Revised: 15 November 2018 / Accepted: 20 November 2018 / Published: 26 November 2018
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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)
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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
Received: 21 September 2018 / Revised: 29 October 2018 / Accepted: 3 November 2018 / Published: 7 November 2018
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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)
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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
Received: 28 September 2018 / Revised: 30 October 2018 / Accepted: 31 October 2018 / Published: 3 November 2018
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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)
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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
Received: 18 September 2018 / Revised: 15 October 2018 / Accepted: 16 October 2018 / Published: 19 October 2018
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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)
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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
Received: 13 July 2018 / Revised: 6 September 2018 / Accepted: 14 September 2018 / Published: 15 September 2018
PDF Full-text (9028 KB) | HTML Full-text | XML Full-text | Supplementary Files
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)
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Review

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Open AccessReview Validity and Reliability of Wearable Sensors for Joint Angle Estimation: A Systematic Review
Sensors 2019, 19(7), 1555; https://doi.org/10.3390/s19071555
Received: 9 February 2019 / Revised: 25 March 2019 / Accepted: 27 March 2019 / Published: 31 March 2019
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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)
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Planned Papers

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Daily in-home gait evaluation based on biometric recognition by deep Learning

University of Missouri

Gait is the most important sign of health in elderly. Moreover, gait speed in particular reflects the overall health of adults over 65. Our group has developed a gait evaluation system based on a depth camera. One of the challenge of the current gait detection system is to differentiate the resident(s) of the apartment from visitors and update the individual gait model(s) accordingly. While many laboratory tested solution have been proposed, we will test our deep learning methodology on gait systems currently running in 100 apartments in Columbia, MO, including TigerPlace

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