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Wearable Sensors Applied in Movement Analysis

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

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 31909

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Special Issue Editors

Dr. Fabien Buisseret

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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:

Go unnoticed for the people wearing them. They should come with wireless connectivity and low-power consumption.
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.
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 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.

Keywords

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

Published Papers (11 papers)

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Editorial

Jump to: Research

4 pages, 210 KiB

Open AccessEditorial

Wearable Sensors Applied in Movement Analysis

by Fabien Buisseret, Frédéric Dierick and Liesbet Van der Perre

Sensors 2022, 22(21), 8239; https://doi.org/10.3390/s22218239 - 27 Oct 2022

Cited by 2 | Viewed by 1481

Abstract

Recent advances in the miniaturization of electronics have resulted in sensors whose sizes and weights are such that they can be attached to living systems without interfering with their natural movements and behaviors [...] Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

Research

Jump to: Editorial

18 pages, 3074 KiB

Open AccessArticle

Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain

by Trung C. Phan, Adrian Pranata, Joshua Farragher, Adam Bryant, Hung T. Nguyen and Rifai Chai

Sensors 2022, 22(17), 6694; https://doi.org/10.3390/s22176694 - 4 Sep 2022

Cited by 9 | Viewed by 2508

Abstract

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy. Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

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14 pages, 1700 KiB

Open AccessArticle

Machine Learning Identifies Chronic Low Back Pain Patients from an Instrumented Trunk Bending and Return Test

by Paul Thiry, Martin Houry, Laurent Philippe, Olivier Nocent, Fabien Buisseret, Frédéric Dierick, Rim Slama, William Bertucci, André Thévenon and Emilie Simoneau-Buessinger

Sensors 2022, 22(13), 5027; https://doi.org/10.3390/s22135027 - 3 Jul 2022

Cited by 8 | Viewed by 2780

Abstract

Nowadays, the better assessment of low back pain (LBP) is an important challenge, as it is the leading musculoskeletal condition worldwide in terms of years of disability. The objective of this study was to evaluate the relevance of various machine learning (ML) algorithms and Sample Entropy (SampEn), which assesses the complexity of motion variability in identifying the condition of low back pain. Twenty chronic low-back pain (CLBP) patients and 20 healthy non-LBP participants performed 1-min repetitive bending (flexion) and return (extension) trunk movements. Analysis was performed using the time series recorded by three inertial sensors attached to the participants. It was found that SampEn was significantly lower in CLBP patients, indicating a loss of movement complexity due to LBP. Gaussian Naive Bayes ML proved to be the best of the various tested algorithms, achieving 79% accuracy in identifying CLBP patients. Angular velocity of flexion movement was the most discriminative feature in the ML analysis. This study demonstrated that: supervised ML and a complexity assessment of trunk movement variability are useful in the identification of CLBP condition, and that simple kinematic indicators are sensitive to this condition. Therefore, ML could be progressively adopted by clinicians in the assessment of CLBP patients. Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

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17 pages, 5605 KiB

Open AccessArticle

Energy–Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices

by Woosoon Jung and Hyung Gyu Lee

Sensors 2022, 22(13), 4801; https://doi.org/10.3390/s22134801 - 25 Jun 2022

Cited by 2 | Viewed by 1812

Abstract

Wearable Internet of Things (IoT) devices can be used efficiently for gesture recognition applications. The nature of these applications requires high recognition accuracy with low energy consumption, which is not easy to solve at the same time. In this paper, we design a finger gesture recognition system using a wearable IoT device. The proposed recognition system uses a light-weight multi-layer perceptron (MLP) classifier which can be implemented even on a low-end micro controller unit (MCU), with a 2-axes flex sensor. To achieve high recognition accuracy with low energy consumption, we first design a framework for the finger gesture recognition system including its components, followed by system-level performance and energy models. Then, we analyze system-level accuracy and energy optimization issues, and explore the numerous design choices to finally achieve energy–accuracy aware finger gesture recognition, targeting four commonly used low-end MCUs. Our extensive simulation and measurements using prototypes demonstrate that the proposed design achieves up to 95.5% recognition accuracy with energy consumption under 2.74 mJ per gesture on a low-end embedded wearable IoT device. We also provide the Pareto-optimal designs among a total of 159 design choices to achieve energy–accuracy aware design points under given energy or accuracy constraints. Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

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14 pages, 2021 KiB

Open AccessArticle

Home-Based Measurements of Dystonia in Cerebral Palsy Using Smartphone-Coupled Inertial Sensor Technology and Machine Learning: A Proof-of-Concept Study

by Dylan den Hartog, Marjolein M. van der Krogt, Sven van der Burg, Ignazio Aleo, Johannes Gijsbers, Laura A. Bonouvrié, Jaap Harlaar, Annemieke I. Buizer and Helga Haberfehlner

Sensors 2022, 22(12), 4386; https://doi.org/10.3390/s22124386 - 9 Jun 2022

Cited by 7 | Viewed by 3141

Abstract

Accurate and reliable measurement of the severity of dystonia is essential for the indication, evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and adolescents with dyskinetic cerebral palsy (CP) is now commonly performed by visual evaluation either directly in the doctor’s office or from video recordings using standardized scales. Both methods lack objectivity and require much time and effort of clinical experts. Only a snapshot of the severity of dyskinetic movements (i.e., choreoathetosis and dystonia) is captured, and they are known to fluctuate over time and can increase with fatigue, pain, stress or emotions, which likely happens in a clinical environment. The goal of this study was to investigate whether it is feasible to use home-based measurements to assess and evaluate the severity of dystonia using smartphone-coupled inertial sensors and machine learning. Video and sensor data during both active and rest situations from 12 patients were collected outside a clinical setting. Three clinicians analyzed the videos and clinically scored the dystonia of the extremities on a 0–4 scale, following the definition of amplitude of the Dyskinesia Impairment Scale. The clinical scores and the sensor data were coupled to train different machine learning models using cross-validation. The average F1 scores (0.67 ± 0.19 for lower extremities and 0.68 ± 0.14 for upper extremities) in independent test datasets indicate that it is possible to detected dystonia automatically using individually trained models. The predictions could complement standard dyskinetic CP measures by providing frequent, objective, real-world assessments that could enhance clinical care. A generalized model, trained with data from other subjects, shows lower F1 scores (0.45 for lower extremities and 0.34 for upper extremities), likely due to a lack of training data and dissimilarities between subjects. However, the generalized model is reasonably able to distinguish between high and lower scores. Future research should focus on gathering more high-quality data and study how the models perform over the whole day. Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

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12 pages, 825 KiB

Open AccessArticle

Head Pitch Angular Velocity Discriminates (Sub-)Acute Neck Pain Patients and Controls Assessed with the DidRen Laser Test

by Renaud Hage, Fabien Buisseret, Martin Houry and Frédéric Dierick

Sensors 2022, 22(7), 2805; https://doi.org/10.3390/s22072805 - 6 Apr 2022

Cited by 3 | Viewed by 2834

Abstract

Understanding neck pain is an important societal issue. Kinematic data from sensors may help to gain insight into the pathophysiological mechanisms associated with neck pain through a quantitative sensorimotor assessment of one patient. The objective of this study was to evaluate the potential usefulness of artificial intelligence with several machine learning (ML) algorithms in assessing neck sensorimotor performance. Angular velocity and acceleration measured by an inertial sensor placed on the forehead during the DidRen laser test in thirty-eight acute and subacute non-specific neck pain (ANSP) patients were compared to forty-two healthy control participants (HCP). Seven supervised ML algorithms were chosen for the predictions. The most informative kinematic features were computed using Sequential Feature Selection methods. The best performing algorithm is the Linear Support Vector Machine with an accuracy of 82% and Area Under Curve of 84%. The best discriminative kinematic feature between ANSP patients and HCP is the first quartile of head pitch angular velocity. This study has shown that supervised ML algorithms could be used to classify ANSP patients and identify discriminatory kinematic features potentially useful for clinicians in the assessment and monitoring of the neck sensorimotor performance in ANSP patients. Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

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10 pages, 441 KiB

Open AccessArticle

High Specificity of Single Inertial Sensor-Supplemented Timed Up and Go Test for Assessing Fall Risk in Elderly Nursing Home Residents

by Frédéric Dierick, Pierre-Loup Stoffel, Gaston Schütz and Fabien Buisseret

Sensors 2022, 22(6), 2339; https://doi.org/10.3390/s22062339 - 17 Mar 2022

Cited by 9 | Viewed by 2694

Abstract

The Timed Up and Go test (TUG) is commonly used to estimate the fall risk in the elderly. Several ways to improve the predictive accuracy of TUG (cameras, multiple sensors, other clinical tests) have already been proposed. Here, we added a single wearable inertial measurement unit (IMU) to capture the residents’ body center-of-mass kinematics in view of improving TUG’s predictive accuracy. The aim is to find out which kinematic variables and residents’ characteristics are relevant for distinguishing faller from non-faller patients. Data were collected in 73 nursing home residents with the IMU placed on the lower back. Acceleration and angular velocity time series were analyzed during different subtasks of the TUG. Multiple logistic regressions showed that total time required, maximum angular velocity at the first half-turn, gender, and use of a walking aid were the parameters leading to the best predictive abilities of fall risk. The predictive accuracy of the proposed new test, called i + TUG, reached a value of 74.0%, with a specificity of 95.9% and a sensitivity of 29.2%. By adding a single wearable IMU to TUG, an accurate and highly specific test is therefore obtained. This method is quick, easy to perform and inexpensive. We recommend to integrate it into daily clinical practice in nursing homes. Full article

(This article belongs to the Special Issue Wearable Sensors Applied in Movement Analysis)

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14 pages, 306 KiB

Open AccessArticle

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 Stefano Lanzi, Joël Boichat, Luca Calanca, Lucia Mazzolai and Davide Malatesta

Sensors 2021, 21(23), 7989; https://doi.org/10.3390/s21237989 - 30 Nov 2021

Cited by 11 | Viewed by 2201

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) 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)

16 pages, 3410 KiB

Open AccessArticle

Wearable IMU-Based Human Activity Recognition Algorithm for Clinical Balance Assessment Using 1D-CNN and GRU Ensemble Model

by Yeon-Wook Kim, Kyung-Lim Joa, Han-Young Jeong and Sangmin Lee

Sensors 2021, 21(22), 7628; https://doi.org/10.3390/s21227628 - 17 Nov 2021

Cited by 31 | Viewed by 3585

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 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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13 pages, 1146 KiB

Open AccessArticle

Connected Skiing: Motion Quality Quantification in Alpine Skiing

by Cory Snyder, Aaron Martínez, Rüdiger Jahnel, Jason Roe and Thomas Stöggl

Sensors 2021, 21(11), 3779; https://doi.org/10.3390/s21113779 - 29 May 2021

Cited by 12 | Viewed by 3215

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 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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9 pages, 1093 KiB

Open AccessCommunication

Detection of Movement Events of Long-Track Speed Skating Using Wearable Inertial Sensors

by Yosuke Tomita, Tomoki Iizuka, Koichi Irisawa and Shigeyuki Imura

Sensors 2021, 21(11), 3649; https://doi.org/10.3390/s21113649 - 24 May 2021

Cited by 12 | Viewed by 3353

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 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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