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Intelligent Wearable Sensor-Based Gait and Movement Analysis
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Intelligent Wearable Sensor-Based Gait and Movement Analysis

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 4881

Special Issue Editors

Dr. Diana Trojaniello

E-Mail Website
Guest Editor
Center for Advanced Technology in Health and Wellbeing, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy
Interests: gait analysis; motion analysis; movement analysis; biomechanics; human movement analysis based on wearable technologies
Dr. Alan Godfrey

E-Mail Website
Guest Editor
Department Computer Science, Northumbria University, Newcastle-upon-Tyne NE1 8ST, UK
Interests: wearable systems; physiological monitoring; digital biomarkers; gait
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the recent growth in technology, wearable technologies are now being widely used for human motion analysis and gait analysis. Wearable smart devices can be applied in new sensing technologies and transducers, signal processing, and artificial intelligence, making them attractive in biomechanics contexts for real-time analysis.

This Special Issue aims to show how intelligent and wearable sensors can be used for human movement, gait analysis, and smart health monitoring.

The topics of interest include but are not limited to:

  • Gait analysis;
  • Human movement analysis;
  • Wearable sensors;
  • Sensing technologies
  • Sensor signal processing;
  • Health monitoring systems;
  • Rehabilitation;
  • Biomechanics. 

Dr. Diana Trojaniello
Dr. Alan Godfrey
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 (5 papers)

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Research

11 pages, 2791 KiB  
Article
The Validity and Reliability of a Real-Time Biofeedback System for Lumbopelvic Control Training in Baseball Players
by Shiu-Min Wang, Po-Hsien Jiang, Kuei-Yuan Chan and Wei-Li Hsu
Sensors 2024, 24(10), 3060; https://0-doi-org.brum.beds.ac.uk/10.3390/s24103060 - 11 May 2024
Viewed by 242
Abstract
Background: This study validates real-time biofeedback for lumbopelvic control training in baseball. The lumbopelvic region is crucial for generating kinetic energy in pitching. Real-time biofeedback enhances training effectiveness and reduces injury risk. The validity and reliability of this system were examined. Purpose: This [...] Read more.
Background: This study validates real-time biofeedback for lumbopelvic control training in baseball. The lumbopelvic region is crucial for generating kinetic energy in pitching. Real-time biofeedback enhances training effectiveness and reduces injury risk. The validity and reliability of this system were examined. Purpose: This study was to investigate the validity and reliability of the real-time biofeedback system for lumbopelvic control training. Methods: Twelve baseball players participated in this study, with data collected in two sessions separated by a week. All participants needed to do the lateral slide exercise and single-leg squat exercise in each session. Pelvic angles detected by the real-time biofeedback system were compared to the three-dimensional motion capture system (VICON) during training sessions. Additionally, pelvic angles measured by the biofeedback system were compared between the two training sessions. Results: The real-time biofeedback system exhibited moderate to strong correlations with VICON in both exercises: lateral slide exercise (r = 0.66–0.88, p < 0.05) and single-leg squat exercise (r = 0.70–0.85, p < 0.05). Good to excellent reliability was observed between the first and second sessions for both exercises: lateral slide exercise (ICC = 0.76–0.97) and single-leg squat exercise (ICC = 0.79–0.90). Conclusions: The real-time biofeedback system for lumbopelvic control training, accurately providing the correct pelvic angle during training, could enhance training effectiveness. Full article
(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)
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16 pages, 3948 KiB  
Article
Gait Pattern Analysis: Integration of a Highly Sensitive Flexible Pressure Sensor on a Wireless Instrumented Insole
by Partha Sarati Das, Daniella Skaf, Lina Rose, Fatemeh Motaghedi, Tricia Breen Carmichael, Simon Rondeau-Gagné and Mohammed Jalal Ahamed
Sensors 2024, 24(9), 2944; https://0-doi-org.brum.beds.ac.uk/10.3390/s24092944 - 6 May 2024
Viewed by 446
Abstract
Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual’s gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric [...] Read more.
Gait phase monitoring wearable sensors play a crucial role in assessing both health and athletic performance, offering valuable insights into an individual’s gait pattern. In this study, we introduced a simple and cost-effective capacitive gait sensor manufacturing approach, utilizing a micropatterned polydimethylsiloxane dielectric layer placed between screen-printed silver electrodes. The sensor demonstrated inherent stretchability and durability, even when the electrode was bent at a 45-degree angle, it maintained an electrode resistance of approximately 3 Ω. This feature is particularly advantageous for gait monitoring applications. Furthermore, the fabricated flexible capacitive pressure sensor exhibited higher sensitivity and linearity at both low and high pressure and displayed very good stability. Notably, the sensors demonstrated rapid response and recovery times for both under low and high pressure. To further explore the capabilities of these new sensors, they were successfully tested as insole-type pressure sensors for real-time gait signal monitoring. The sensors displayed a well-balanced combination of sensitivity and response time, making them well-suited for gait analysis. Beyond gait analysis, the proposed sensor holds the potential for a wide range of applications within biomedical, sports, and commercial systems where soft and conformable sensors are preferred. Full article
(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)
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18 pages, 7523 KiB  
Article
Unsupervised Cluster Analysis of Walking Activity Data for Healthy Individuals and Individuals with Lower Limb Amputation
by Alexander Jamieson, Laura Murray, Vladimir Stankovic, Lina Stankovic and Arjan Buis
Sensors 2023, 23(19), 8164; https://0-doi-org.brum.beds.ac.uk/10.3390/s23198164 - 29 Sep 2023
Viewed by 896
Abstract
This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and [...] Read more.
This is the first investigation to perform an unsupervised cluster analysis of activities performed by individuals with lower limb amputation (ILLAs) and individuals without gait impairment, in free-living conditions. Eight individuals with no gait impairments and four ILLAs wore a thigh-based accelerometer and walked on an improvised route across a variety of terrains in the vicinity of their homes. Their physical activity data were clustered to extract ‘unique’ groupings in a low-dimension feature space in an unsupervised learning approach, and an algorithm was created to automatically distinguish such activities. After testing three dimensionality reduction methods—namely, principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP)—we selected tSNE due to its performance and stable outputs. Cluster formation of activities via DBSCAN only occurred after the data were reduced to two dimensions via tSNE and contained only samples for a single individual. Additionally, through analysis of the t-SNE plots, appreciable clusters in walking-based activities were only apparent with ground walking and stair ambulation. Through a combination of density-based clustering and analysis of cluster distance and density, a novel algorithm inspired by the t-SNE plots, resulting in three proposed and validated hypotheses, was able to identify cluster formations that arose from ground walking and stair ambulation. Low dimensional clustering of activities has thus been found feasible when analyzing individual sets of data and can currently recognize stair and ground walking ambulation. Full article
(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)
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11 pages, 17027 KiB  
Communication
Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques
by Giuseppina Monti and Luciano Tarricone
Sensors 2023, 23(18), 7748; https://0-doi-org.brum.beds.ac.uk/10.3390/s23187748 - 8 Sep 2023
Viewed by 681
Abstract
In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results [...] Read more.
In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition. Full article
(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)
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19 pages, 1671 KiB  
Article
Improving Inertial Sensor-Based Activity Recognition in Neurological Populations
by Yunus Celik, M. Fatih Aslan, Kadir Sabanci, Sam Stuart, Wai Lok Woo and Alan Godfrey
Sensors 2022, 22(24), 9891; https://0-doi-org.brum.beds.ac.uk/10.3390/s22249891 - 15 Dec 2022
Cited by 5 | Viewed by 1937
Abstract
Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial [...] Read more.
Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson’s disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued. Full article
(This article belongs to the Special Issue Intelligent Wearable Sensor-Based Gait and Movement Analysis)
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