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Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle

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

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 62835

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Dr. Giuseppe Vannozzi

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Department of Movement, Human and Health Science, Università degli Studi di Roma "Foro Italico", Piazza L. de Bosis 6, 00135 Rome, Italy
Interests: motion analysis; inertial sensors; data mining; wearable sensors; bioengineering; sport biomechanics; signal processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Silvia Fantozzi

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1. Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, University of Bologna, 40136 Bologna, Italy
2. Health Sciences and Technologies - Interdepartmental Center for Industrial Research, University of Bologna, 40064 Ozzano dell’Emilia, Italy
Interests: biomechanics; movement analysis; sport performance; injury prevention; aquatic activities

Prof. Dr. DirkJan H.E.J. Veeger

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Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands
Interests: biomechanics; upper extremity; kinematics; wheelchair propulsion; sport biomechanics

Special Issue Information

Dear Colleagues,

Recent advancements in wearable motion sensor and textiles technology have definitely pushed forward research and applications in different fields. However, despite the field’s growing maturity, there remain numerous challenges in a broad range of areas that require research effort in order to further the applicability of this technology in everyday contexts.

To highlight latest methodological advancements as well as innovative applications and developments in this field, we invite you to consider submitting a manuscript to our upcoming Special Issue, “Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity, and a Healthy Lifestyle”. Both research papers and review articles will be considered. We welcome submissions spanning topics across motion sensor devices development, definition and validation of new protocols involving wearable technologies, and of algorithms to extract new parameters of interest from wearable devices.

This Special Issue aims at reporting the latest research updates in wearable motion sensors and textiles, and their applications in human movement across the lifespan. Possible topics include but are not limited to:

Wearable sensors, protocols, and algorithms for human movement analysis;
Wearable sensors, protocols, and algorithms for motor development in childhood;
Wearable sensors, protocols, and algorithms for feedback in sports activity;
Wearable sensors, protocols, and algorithms for motor activity assessment in daily life;
Wearable motion sensors and textiles, protocols and algorithms in aging research;
Wearable motion sensors and textiles, protocols and algorithms in injury prevention;
Wearable motion sensors and textiles, protocols and algorithms for healthy lifestyle promotion.

Dr. Giuseppe Vannozzi
Dr. Silvia Fantozzi
Prof. Dr. H.E.J. (DirkJan) Veeger
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 submissions that pass pre-check are 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 2600 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.

Published Papers (13 papers)

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18 pages, 7881 KiB

Open AccessArticle

Development of Instrumented Running Prosthetic Feet for the Collection of Track Loads on Elite Athletes

by Nicola Petrone, Gianfabio Costa, Gianmario Foscan, Antonio Gri, Leonardo Mazzanti, Gianluca Migliore and Andrea Giovanni Cutti

Sensors 2020, 20(20), 5758; https://doi.org/10.3390/s20205758 - 10 Oct 2020

Cited by 7 | Viewed by 4147

Abstract

Knowledge of loads acting on running specific prostheses (RSP), and in particular on running prosthetic feet (RPF), is crucial for evaluating athletes’ technique, designing safe feet, and biomechanical modelling. The aim of this work was to develop a J-shaped and a C-shaped wearable instrumented running prosthetic foot (iRPF) starting from commercial RPF, suitable for load data collection on the track. The sensing elements are strain gauge bridges mounted on the foot in a configuration that allows decoupling loads parallel and normal to the socket-foot clamp during the stance phase. The system records data on lightweight athlete-worn loggers and transmits them via Wi-Fi to a base station for real-time monitoring. iRPF calibration procedure and static and dynamic validation of predicted ground-reaction forces against those measured by a force platform embedded in the track are reported. The potential application of this wearable system in estimating determinants of sprint performance is presented. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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26 pages, 1254 KiB

Open AccessArticle

w-HAR: An Activity Recognition Dataset and Framework Using Low-Power Wearable Devices

by Ganapati Bhat, Nicholas Tran, Holly Shill and Umit Y. Ogras

Sensors 2020, 20(18), 5356; https://doi.org/10.3390/s20185356 - 18 Sep 2020

Cited by 46 | Viewed by 4963

Abstract

Human activity recognition (HAR) is growing in popularity due to its wide-ranging applications in patient rehabilitation and movement disorders. HAR approaches typically start with collecting sensor data for the activities under consideration and then develop algorithms using the dataset. As such, the success of algorithms for HAR depends on the availability and quality of datasets. Most of the existing work on HAR uses data from inertial sensors on wearable devices or smartphones to design HAR algorithms. However, inertial sensors exhibit high noise that makes it difficult to segment the data and classify the activities. Furthermore, existing approaches typically do not make their data available publicly, which makes it difficult or impossible to obtain comparisons of HAR approaches. To address these issues, we present wearable HAR (w-HAR) which contains labeled data of seven activities from 22 users. Our dataset’s unique aspect is the integration of data from inertial and wearable stretch sensors, thus providing two modalities of activity information. The wearable stretch sensor data allows us to create variable-length segment data and ensure that each segment contains a single activity. We also provide a HAR framework to use w-HAR to classify the activities. To this end, we first perform a design space exploration to choose a neural network architecture for activity classification. Then, we use two online learning algorithms to adapt the classifier to users whose data are not included at design time. Experiments on the w-HAR dataset show that our framework achieves 95% accuracy while the online learning algorithms improve the accuracy by as much as 40%. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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13 pages, 2648 KiB

Open AccessArticle

Validation of Fabric-Based Thigh-Wearable EMG Sensors and Oximetry for Monitoring Quadriceps Activity during Strength and Endurance Exercises

by Riccardo Di Giminiani, Marco Cardinale, Marco Ferrari and Valentina Quaresima

Sensors 2020, 20(17), 4664; https://doi.org/10.3390/s20174664 - 19 Aug 2020

Cited by 23 | Viewed by 5276

Abstract

Muscle oximetry based on near-infrared spectroscopy (NIRS) and electromyography (EMG) techniques in adherent clothing might be used to monitor the muscular activity of selected muscle groups while exercising. The fusion of these wearable technologies in sporting garments can allow the objective assessment of the quality and the quantity of the muscle activity as well as the continuous monitoring of exercise programs. Several prototypes integrating EMG and NIRS have been developed previously; however, most devices presented the limitations of not measuring regional muscle oxyhemoglobin saturation and did not embed textile sensors for EMG. The purpose of this study was to compare regional muscle oxyhemoglobin saturation and surface EMG data, measured under resting and dynamic conditions (treadmill run and strength exercises) by a recently developed wearable integrated quadriceps muscle oximetry/EMG system adopting smart textiles for EMG, with those obtained by using two “gold standard” commercial instrumentations for EMG and muscle oximetry. The validity and agreement between the wearable integrated muscle oximetry/EMG system and the “gold standard” instrumentations were assessed by using the Bland-Altman agreement plots to determine the bias. The results support the validity of the data provided by the wearable electronic garment developed purposely for the quadriceps muscle group and suggest the potential of using such device to measure strength and endurance exercises in vivo in various populations. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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29 pages, 3223 KiB

Open AccessArticle

Wearable Sensor Network for Biomechanical Overload Assessment in Manual Material Handling

by Paolo Giannini, Giulia Bassani, Carlo Alberto Avizzano and Alessandro Filippeschi

Sensors 2020, 20(14), 3877; https://doi.org/10.3390/s20143877 - 11 Jul 2020

Cited by 20 | Viewed by 4595

Abstract

The assessment of risks due to biomechanical overload in manual material handling is nowadays mainly based on observational methods in which an expert rater visually inspects videos of the working activity. Currently available sensing wearable technologies for motion and muscular activity capture enables to advance the risk assessment by providing reliable, repeatable, and objective measures. However, existing solutions do not address either a full body assessment or the inclusion of measures for the evaluation of the effort. This article proposes a novel system for the assessment of biomechanical overload, capable of covering all areas of ISO 11228, that uses a sensor network composed of inertial measurement units (IMU) and electromyography (EMG) sensors. The proposed method is capable of gathering and processing data from three IMU-based motion capture systems and two EMG capture devices. Data are processed to provide both segmentation of the activity and ergonomic risk score according to the methods reported in the ISO 11228 and the TR 12295. The system has been tested on a challenging outdoor scenario such as lift-on/lift-off of containers on a cargo ship. A comparison of the traditional evaluation method and the proposed one shows the consistency of the proposed system, its time effectiveness, and its potential for deeper analyses that include intra-subject and inter-subjects variability as well as a quantitative biomechanical analysis. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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13 pages, 2717 KiB

Open AccessArticle

Wearable Wheelchair Mobility Performance Measurement in Basketball, Rugby, and Tennis: Lessons for Classification and Training

by Rienk M. A. van der Slikke, Monique A. M. Berger, Daan J. J. Bregman and Dirkjan H. E. J. Veeger

Sensors 2020, 20(12), 3518; https://doi.org/10.3390/s20123518 - 21 Jun 2020

Cited by 27 | Viewed by 5471

Abstract

Athlete impairment level is an important factor in wheelchair mobility performance (WMP) in sports. Classification systems, aimed to compensate impairment level effects on performance, vary between sports. Improved understanding of resemblances and differences in WMP between sports could aid in optimizing the classification methodology. Furthermore, increased performance insight could be applied in training and wheelchair optimization. The wearable sensor-based wheelchair mobility performance monitor (WMPM) was used to measure WMP of wheelchair basketball, rugby and tennis athletes of (inter-)national level during match-play. As hypothesized, wheelchair basketball athletes show the highest average WMP levels and wheelchair rugby the lowest, whereas wheelchair tennis athletes range in between for most outcomes. Based on WMP profiles, wheelchair basketball requires the highest performance intensity, whereas in wheelchair tennis, maneuverability is the key performance factor. In wheelchair rugby, WMP levels show the highest variation comparable to the high variation in athletes’ impairment levels. These insights could be used to direct classification and training guidelines, with more emphasis on intensity for wheelchair basketball, focus on maneuverability for wheelchair tennis and impairment-level based training programs for wheelchair rugby. Wearable technology use seems a prerequisite for further development of wheelchair sports, on the sports level (classification) and on individual level (training and wheelchair configuration). Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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16 pages, 4397 KiB

Open AccessArticle

Inertial Sensor-Based Motion Tracking in Football with Movement Intensity Quantification

by Erik Wilmes, Cornelis J. de Ruiter, Bram J. C. Bastiaansen, Jasper F. J. A. van Zon, Riemer J. K. Vegter, Michel S. Brink, Edwin A. Goedhart, Koen A. P. M. Lemmink and Geert J. P. Savelsbergh

Sensors 2020, 20(9), 2527; https://doi.org/10.3390/s20092527 - 29 Apr 2020

Cited by 28 | Viewed by 5880

Abstract

Inertial sensor-based measurements of lower body kinematics in football players may improve physical load estimates during training sessions and matches. However, the validity of inertial-based motion analysis systems is specific to both the type of movement and the intensity at which movements are executed. Importantly, such a system should be relatively simple, so it can easily be used in daily practice. This paper introduces an easy-to-use inertial-based motion analysis system and evaluates its validity using an optoelectronic motion analysis system as a gold standard. The system was validated in 11 football players for six different football specific movements that were executed at low, medium, and maximal intensity. Across all movements and intensities, the root mean square differences (means ± SD) for knee and hip flexion/extension angles were 5.3° ± 3.4° and 8.0° ± 3.5°, respectively, illustrating good validity with the gold standard. In addition, mean absolute flexion/extension angular velocities significantly differed between the three movement intensities. These results show the potential to use the inertial based motion analysis system in football practice to obtain lower body kinematics and to quantify movement intensity, which both may improve currently used physical load estimates of the players. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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24 pages, 9321 KiB

Open AccessArticle

Ski Jumping Trajectory Reconstruction Using Wearable Sensors via Extended Rauch-Tung-Striebel Smoother with State Constraints

by Xiang Fang, Benedikt Grüter, Patrick Piprek, Veronica Bessone, Johannes Petrat and Florian Holzapfel

Sensors 2020, 20(7), 1995; https://doi.org/10.3390/s20071995 - 2 Apr 2020

Cited by 5 | Viewed by 4585

Abstract

To satisfy an increasing demand to reconstruct an athlete’s motion for performance analysis, this paper proposes a new method for reconstructing the position and velocity in the context of ski jumping trajectories. Therefore, state-of-the-art wearable sensors, including an inertial measurement unit, a magnetometer, and a GPS logger are used. The method employs an extended Rauch-Tung-Striebel smoother with state constraints to estimate state information offline from recorded raw measurements. In comparison to the classic inertial navigation system and GPS integration solution, the proposed method includes additional geometric shape information of the ski jumping hill, which are modeled as soft constraints and embedded into the estimation framework to improve the position and velocity estimation accuracy. Results for both simulated measurement data and real measurement data demonstrate the effectiveness of the proposed method. Moreover, a comparison between jump lengths obtained from the proposed method and video recordings shows the relative root-mean-square error of the reconstructed jump length is below 1.5 m depicting the accuracy of the algorithm. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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13 pages, 1520 KiB

Open AccessArticle

Timing and Modulation of Activity in the Lower Limb Muscles During Indoor Rowing: What Are the Key Muscles to Target in FES-Rowing Protocols?

by Taian M. Vieira, Giacinto Luigi Cerone, Costanza Stocchi, Morgana Lalli, Brian Andrews and Marco Gazzoni

Sensors 2020, 20(6), 1666; https://doi.org/10.3390/s20061666 - 17 Mar 2020

Cited by 2 | Viewed by 3435

Abstract

The transcutaneous stimulation of lower limb muscles during indoor rowing (FES Rowing) has led to a new sport and recreation and significantly increased health benefits in paraplegia. Stimulation is often delivered to quadriceps and hamstrings; this muscle selection seems based on intuition and not biomechanics and is likely suboptimal. Here, we sample surface EMGs from 20 elite rowers to assess which, when, and how muscles are activated during indoor rowing. From EMG amplitude we specifically quantified the onset of activation and silencing, the duration of activity and how similarly soleus, gastrocnemius medialis, tibialis anterior, rectus femoris, vastus lateralis and medialis, semitendinosus, and biceps femoris muscles were activated between limbs. Current results revealed that the eight muscles tested were recruited during rowing, at different instants and for different durations. Rectus and biceps femoris were respectively active for the longest and briefest periods. Tibialis anterior was the only muscle recruited within the recovery phase. No side differences in the timing of muscle activity were observed. Regression analysis further revealed similar, bilateral modulation of activity. The relevance of these results in determining which muscles to target during FES Rowing is discussed. Here, we suggest a new strategy based on the stimulation of vasti and soleus during drive and of tibialis anterior during recovery. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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17 pages, 3719 KiB

Open AccessArticle

Hurdle Clearance Detection and Spatiotemporal Analysis in 400 Meters Hurdles Races Using Shoe-Mounted Magnetic and Inertial Sensors

by Mathieu Falbriard, Maurice Mohr and Kamiar Aminian

Sensors 2020, 20(2), 354; https://doi.org/10.3390/s20020354 - 8 Jan 2020

Cited by 5 | Viewed by 6965

Abstract

This research aimed to determine whether: (1) shoe-worn magnetic and inertial sensors can be used to detect hurdle clearance and identify the leading leg in 400-m hurdles, and (2) to provide an analysis of the hurdlers’ spatiotemporal parameters in the intervals defined by the hurdles’ position. The data set is composed of MIMU recordings of 15 athletes in a competitive environment. The results show that the method based on the duration of the flight phase was able to detect hurdle clearance and identify the leading leg with 100% accuracy. Moreover, by combining the swing phase duration with the orientation of the foot, we achieved, in unipedal configuration, 100% accuracy in hurdle clearance detection, and 99.7% accuracy in the identification of the leading leg. Finally, this study provides statistical evidence that contact time significantly increases, while speed and step frequency significantly decrease with time during 400 m hurdle races. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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18 pages, 2752 KiB

Open AccessArticle

Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

by Mohsen Gholami, Ahmad Rezaei, Tyler J. Cuthbert, Christopher Napier and Carlo Menon

Sensors 2019, 19(23), 5325; https://doi.org/10.3390/s19235325 - 3 Dec 2019

Cited by 33 | Viewed by 5777

Abstract

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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26 pages, 1438 KiB

Open AccessArticle

Characterizing Word Embeddings for Zero-Shot Sensor-Based Human Activity Recognition

by Moe Matsuki, Paula Lago and Sozo Inoue

Sensors 2019, 19(22), 5043; https://doi.org/10.3390/s19225043 - 19 Nov 2019

Cited by 15 | Viewed by 5456

Abstract

In this paper, we address Zero-shot learning for sensor activity recognition using word embeddings. The goal of Zero-shot learning is to estimate an unknown activity class (i.e., an activity that does not exist in a given training dataset) by learning to recognize components of activities expressed in semantic vectors. The existing zero-shot methods use mainly 2 kinds of representation as semantic vectors, attribute vector and embedding word vector. However, few zero-shot activity recognition methods based on embedding vector have been studied; especially for sensor-based activity recognition, no such studies exist, to the best of our knowledge. In this paper, we compare and thoroughly evaluate the Zero-shot method with different semantic vectors: (1) attribute vector, (2) embedding vector, and (3) expanded embedding vector and analyze their correlation to performance. Our results indicate that the performance of the three spaces is similar but the use of word embedding leads to a more efficient method, since this type of semantic vector can be generated automatically. Moreover, our suggested method achieved higher accuracy than attribute-vector methods, in cases when there exist similar information in both the given sensor data and in the semantic vector; the results of this study help select suitable classes and sensor data to build a training dataset. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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15 pages, 4654 KiB

Open AccessTechnical Note

A Complementary Filter Design on SE(3) to Identify Micro-Motions during 3D Motion Tracking

by Gia-Hoang Phan, Clint Hansen, Paolo Tommasino, Asif Hussain, Domenico Formica and Domenico Campolo

Sensors 2020, 20(20), 5864; https://doi.org/10.3390/s20205864 - 16 Oct 2020

Cited by 4 | Viewed by 2468

Abstract

In 3D motion capture, multiple methods have been developed in order to optimize the quality of the captured data. While certain technologies, such as inertial measurement units (IMU), are mostly suitable for 3D orientation estimation at relatively high frequencies, other technologies, such as marker-based motion capture, are more suitable for 3D position estimations at a lower frequency range. In this work, we introduce a complementary filter that complements 3D motion capture data with high-frequency acceleration signals from an IMU. While the local optimization reduces the error of the motion tracking, the additional accelerations can help to detect micro-motions that are useful when dealing with high-frequency human motions or robotic applications. The combination of high-frequency accelerometers improves the accuracy of the data and helps to overcome limitations in motion capture when micro-motions are not traceable with 3D motion tracking system. In our experimental evaluation, we demonstrate the improvements of the motion capture results during translational, rotational, and combined movements. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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11 pages, 1394 KiB

Open AccessLetter

Does Curved Walking Sharpen the Assessment of Gait Disorders? An Instrumented Approach Based on Wearable Inertial Sensors

by Valeria Belluscio, Elena Bergamini, Marco Tramontano, Rita Formisano, Maria Gabriella Buzzi and Giuseppe Vannozzi

Sensors 2020, 20(18), 5244; https://doi.org/10.3390/s20185244 - 14 Sep 2020

Cited by 19 | Viewed by 2693

Abstract

Gait and balance assessment in the clinical context mainly focuses on straight walking. Despite that curved trajectories and turning are commonly faced in our everyday life and represent a challenge for people with gait disorders. The adoption of curvilinear trajectories in the rehabilitation practice could have important implications for the definition of protocols tailored on individual’s needs. The aim of this study was to contribute toward the quantitative characterization of straight versus curved walking using an ecological approach and focusing on healthy and neurological populations. Twenty healthy adults (control group (CG)) and 20 patients with Traumatic Brain Injury (TBI) (9 severe, sTBI-S, and 11 very severe, sTBI-VS) performed a 10 m and a Figure-of-8 Walk Test while wearing four inertial sensors that were located on both tibiae, sternum and pelvis. Spatiotemporal and gait quality indices that were related to locomotion stability, symmetry, and smoothness were obtained. The results show that spatiotemporal, stability, and symmetry-related gait patterns are challenged by curved walking both in healthy subjects and sTBI-S, whereas no difference was displayed for sTBI-VS. The use of straight walking alone to assess gait disorders is thus discouraged, particularly in patients with good walking abilities, in favor of the adoption of complementary tests that were also based on curved paths. Full article

(This article belongs to the Special Issue Wearable Motion Sensors and Textiles for Human Movement Analysis, Motor Activity and Healthy Lifestyle)

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