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Special Issue "Wearable Sensors Applied in Movement Analysis"

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

Deadline for manuscript submissions: 30 April 2022.

Special Issue Editors

Dr. Fabien Buisseret
E-Mail Website
Guest Editor
1. CeREF, Chaussée de Binche 159, 7000 Mons, Belgium
2. Service de Physique Nucléaire et Subnucléaire, Université de Mons, UMONS Research Institute for Complex Systems, 20 Place du Parc, 7000 Mons, Belgium
Interests: theoretical physics; hadrons; mechanics; fractal analysis; motion analysis; kinematics; modelling complex systems; biomechanics
Special Issues, Collections and Topics in MDPI journals
Dr. Frédéric Dierick
E-Mail Website
Guest Editor
Centre National de Rééducation Fonctionnelle et de Réadaptation – Rehazenter, Laboratoire d'Analyse du Mouvement et de la Posture(LAMP), Luxembourg, Luxembourg
Interests: rehabilitation; ergonomy; kinesytherapy; gait analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Liesbet Van der Perre
E-Mail Website
Guest Editor
DraMCo lab of the Electrical Engineering Department, KU Leuven, 9000 Ghent, Belgium.
Interests: wireless connectivity; IoT; low-power systems; embedded systems; connected sensors

Special Issue Information

Dear colleagues,

Recent advances in miniaturization have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Moreover, following the SARS-CoV 2 crisis, we expect that the interest in devices favoring telemedicine such as low-cost wearable sensors will become much more critical.

Wearable sensors should:

  1. Go unnoticed for the people wearing them. They should come with wireless connectivity and low-power consumption.
  2. Be intuitive in their installation. The designed systems should offer high-performance body fixation solutions so that they can be easily accepted by their user. Moreover, the electronics system should be self-calibrating and operating.
  3. Deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.

You are invited to submit articles that propose solutions addressing the above challenges to this Special Issue of Sensors: “Wearable Sensors Applied in Movement Analysis”. Examples of accepted topics are:

  • Clinical applications of wearable sensors in movement evaluation;
  • Gait analysis including fall detection and movement recognition;
  • Calibration methods in real-life conditions;
  • Embedded signal processing;
  • Use of wearable sensors in telemedicine;
  • Wireless transmission and data storage;
  • Applications of wearable sensors in rehabilitation (biofeedback, home exercise);
  • Sensor design, autonomy, body fixation, and acceptability;
  • Trust aspects in sensor-based medicine.

The Guest Editors thank the European Regional Development Fund (Interreg FWVl NOMADe) for supporting their editing and publication activities in the frame of the NOMADe project.

Dr. Fabien Buisseret
Dr. Frédéric Dierick
Prof. Liesbet Van der Perre
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 2200 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

  •  Wearable sensor 
  • Movement analysis 
  • Telemedicine 
  • Diagnostics and evaluation 
  • Acceptability 
  • Real-life use

Published Papers (4 papers)

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Research

Article
Supervised Exercise Training Improves 6 min Walking Distance and Modifies Gait Pattern during Pain-Free Walking Condition in Patients with Symptomatic Lower Extremity Peripheral Artery Disease
by , , , and
Sensors 2021, 21(23), 7989; https://doi.org/10.3390/s21237989 (registering DOI) - 30 Nov 2021
Abstract
This study aimed to investigate the effects of supervised exercise training (SET) on spatiotemporal gait and foot kinematics parameters in patients with symptomatic lower extremity peripheral artery disease (PAD) during a 6 min walk test. Symptomatic patients with chronic PAD (Fontaine stage II) [...] Read more.
This study aimed to investigate the effects of supervised exercise training (SET) on spatiotemporal gait and foot kinematics parameters in patients with symptomatic lower extremity peripheral artery disease (PAD) during a 6 min walk test. Symptomatic patients with chronic PAD (Fontaine stage II) following a 3 month SET program were included. Prior to and following SET, a 6 min walk test was performed to assess the 6 min walking distance (6MWD) of each patient. During this test, spatiotemporal gait and foot kinematics parameters were assessed during pain-free and painful walking conditions. Twenty-nine patients with PAD (65.4 ± 9.9 years.) were included. The 6MWD was significantly increased following SET (+10%; p ≤ 0.001). The walking speed (+8%) and stride frequency (+5%) were significantly increased after SET (p ≤ 0.026). The stride length was only significantly increased during the pain-free walking condition (+4%, p = 0.001), whereas no significant differences were observed during the condition of painful walking. Similarly, following SET, the relative duration of the loading response increased (+12%), the relative duration of the foot-flat phase decreased (−3%), and the toe-off pitch angle significantly increased (+3%) during the pain-free walking condition alone (p ≤ 0.05). A significant positive correlation was found between changes in the stride length (r = 0.497, p = 0.007) and stride frequency (r = 0.786, p ≤ 0.001) during pain-free walking condition and changes in the 6MWD. A significant negative correlation was found between changes in the foot-flat phase during pain-free walking condition and changes in the 6MWD (r = −0.567, p = 0.002). SET was found to modify the gait pattern of patients with symptomatic PAD, and many of these changes were found to occur during pain-free walking. The improvement in individuals’ functional 6 min walk test was related to changes in their gait pattern. Full article
(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)
Article
Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model
Sensors 2021, 21(22), 7628; https://doi.org/10.3390/s21227628 - 17 Nov 2021
Viewed by 232
Abstract
In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to [...] Read more.
In this study, a wearable inertial measurement unit system was introduced to assess patients via the Berg balance scale (BBS), a clinical test for balance assessment. For this purpose, an automatic scoring algorithm was developed. The principal aim of this study is to improve the performance of the machine-learning-based method by introducing a deep-learning algorithm. A one-dimensional (1D) convolutional neural network (CNN) and a gated recurrent unit (GRU) that shows good performance in multivariate time-series data were used as model components to find the optimal ensemble model. Various structures were tested, and a stacking ensemble model with a simple meta-learner after two 1D-CNN heads and one GRU head showed the best performance. Additionally, model performance was enhanced by improving the dataset via preprocessing. The data were down sampled, an appropriate sampling rate was found, and the training and evaluation times of the model were improved. Using an augmentation process, the data imbalance problem was solved, and model accuracy was improved. The maximum accuracy of 14 BBS tasks using the model was 98.4%, which is superior to the results of previous studies. Full article
(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)
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Article
Connected Skiing: Motion Quality Quantification in Alpine Skiing
Sensors 2021, 21(11), 3779; https://doi.org/10.3390/s21113779 - 29 May 2021
Viewed by 851
Abstract
Recent developments in sensing technology have made wearable computing smaller and cheaper. While many wearable technologies aim to quantify motion, there are few which aim to qualify motion. (2) To develop a wearable system to quantify motion quality during alpine skiing, IMUs were [...] Read more.
Recent developments in sensing technology have made wearable computing smaller and cheaper. While many wearable technologies aim to quantify motion, there are few which aim to qualify motion. (2) To develop a wearable system to quantify motion quality during alpine skiing, IMUs were affixed to the ski boots of nineteen expert alpine skiers while they completed a set protocol of skiing styles, included carving and drifting in long, medium, and short radii. The IMU data were processed according to the previously published skiing activity recognition chain algorithms for turn segmentation, enrichment, and turn style classification Principal component models were learned on the time series variables edge angle, symmetry, radial force, and speed to identify the sources of variability in a subset of reference skiers. The remaining data were scored by comparing the PC score distributions of variables to the reference dataset. (3) The algorithm was able to differentiate between an expert and beginner skier, but not between an expert and a ski instructor, or a ski instructor and a beginner. (4) The scoring algorithm is a novel concept to quantify motion quality but is limited by the accuracy and relevance of the input data. Full article
(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)
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Communication
Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors
Sensors 2021, 21(11), 3649; https://doi.org/10.3390/s21113649 - 24 May 2021
Viewed by 960
Abstract
Inertial measurement units (IMUs) have been used increasingly to characterize long-track speed skating. We aimed to estimate the accuracy of IMUs for use in phase identification of long-track speed skating. Twelve healthy competitive athletes on a university long-track speed skating team participated in [...] Read more.
Inertial measurement units (IMUs) have been used increasingly to characterize long-track speed skating. We aimed to estimate the accuracy of IMUs for use in phase identification of long-track speed skating. Twelve healthy competitive athletes on a university long-track speed skating team participated in this study. Foot pressure, acceleration and knee joint angle were recorded during a 1000-m speed skating trial using the foot pressure system and IMUs. The foot contact and foot-off timing were identified using three methods (kinetic, acceleration and integrated detection) and the stance time was also calculated. Kinetic detection was used as the gold standard measure. Repeated analysis of variance, intra-class coefficients (ICCs) and Bland-Altman plots were used to estimate the extent of agreement between the detection methods. The stance time computed using the acceleration and integrated detection methods did not differ by more than 3.6% from the gold standard measure. The ICCs ranged between 0.657 and 0.927 for the acceleration detection method and 0.700 and 0.948 for the integrated detection method. The limits of agreement were between 90.1% and 96.1% for the average stance time. Phase identification using acceleration and integrated detection methods is valid for evaluating the kinematic characteristics during long-track speed skating. Full article
(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)
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