Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Artificial-Intelligence-Enhanced Wearable Sensing Technologies for Biomechanical and Physiological Monitoring and...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial-Intelligence-Enhanced Wearable Sensing Technologies for Biomechanical and Physiological Monitoring and Analysis

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2445

Special Issue Editors

Prof. Dr. Del P. Wong

E-Mail Website
Guest Editor
College of Education, Psychology and Social Work, Flinders University, Adelaide, Australia
Interests: sport biomechanics; movement science; exercise in elderly
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Javen Qinfeng Shi

E-Mail Website
Guest Editor
School of Computer and Mathematical Sciences, University of Adelaide, Adelaide, Australia
Interests: artificial intelligence (AI); machine learning; causality; responsible AI; modelling variables of symptoms and diseases

Special Issue Information

Dear Colleagues,

In this Special Issue, we focus on how wearable sensors are being employed to monitor and analyze the biomechanical and/or physiological behavior of elderly and other special populations. Another objective of this Special Issue is to provide a comprehensive overview of the application of artificial intelligence (AI) and machine learning in healthcare by quantifying the state of progress in terms of their utilization in biomechanics and human physiology.

We will accept manuscripts that address any of the following topics: the use of wearable sensors to collect biomechanical and/or physiological data, handle signal and noise, and process and analyze data, and how these processes are enhanced by the utilization of artificial intelligence (AI) and/or machine learning. One example is to develop a machine learning model that recognizes individuals’ health status or potential issues using the signals of wearable sensors, and offers personalized lifestyle recommendations (e.g., exercise, diets, supplements) to enhance wellbeing by learning from data.

Regarding the studied population, we are interested in elderly individuals, individuals with disabilities (e.g., mobility impairments, visual or hearing impairments, cognitive disabilities, etc), and individuals with chronic illnesses (e.g., stroke, diabetes, cardiovascular diseases, respiratory disorders, autoimmune disorders, etc). Studies focusing on other special populations are also welcome.

Prof. Dr. Del P. Wong
Prof. Dr. Javen Qinfeng Shi
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
  • machine learning
  • artificial intelligence
  • gait
  • healthcare
  • older adult
  • stroke
  • clinical
  • patient
  • rehabilitation

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 5310 KiB  
Article
Estimation of Shoulder Joint Rotation Angle Using Tablet Device and Pose Estimation Artificial Intelligence Model
by Shunsaku Takigami, Atsuyuki Inui, Yutaka Mifune, Hanako Nishimoto, Kohei Yamaura, Tatsuo Kato, Takahiro Furukawa, Shuya Tanaka, Masaya Kusunose, Yutaka Ehara and Ryosuke Kuroda
Sensors 2024, 24(9), 2912; https://doi.org/10.3390/s24092912 - 02 May 2024
Viewed by 250
Abstract
Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and [...] Read more.
Traditionally, angle measurements have been performed using a goniometer, but the complex motion of shoulder movement has made these measurements intricate. The angle of rotation of the shoulder is particularly difficult to measure from an upright position because of the complicated base and moving axes. In this study, we attempted to estimate the shoulder joint internal/external rotation angle using the combination of pose estimation artificial intelligence (AI) and a machine learning model. Videos of the right shoulder of 10 healthy volunteers (10 males, mean age 37.7 years, mean height 168.3 cm, mean weight 72.7 kg, mean BMI 25.6) were recorded and processed into 10,608 images. Parameters were created using the coordinates measured from the posture estimation AI, and these were used to train the machine learning model. The measured values from the smartphone’s angle device were used as the true values to create a machine learning model. When measuring the parameters at each angle, we compared the performance of the machine learning model using both linear regression and Light GBM. When the pose estimation AI was trained using linear regression, a correlation coefficient of 0.971 was achieved, with a mean absolute error (MAE) of 5.778. When trained with Light GBM, the correlation coefficient was 0.999 and the MAE was 0.945. This method enables the estimation of internal and external rotation angles from a direct-facing position. This approach is considered to be valuable for analyzing motor movements during sports and rehabilitation. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Wearable Sensing Technologies for Biomechanical and Physiological Monitoring and Analysis)
►▼ Show Figures

Figure 1

17 pages, 3583 KiB  
Article
Impact of Fatigue on Ergonomic Risk Scores and Foot Kinetics: A Field Study Employing Inertial and In-Shoe Plantar Pressure Measurement Devices
by Steven Simon, Jonas Dully, Carlo Dindorf, Eva Bartaguiz, Stephan Becker and Michael Fröhlich
Sensors 2024, 24(4), 1175; https://doi.org/10.3390/s24041175 - 10 Feb 2024
Cited by 1 | Viewed by 882
Abstract
(1) Background: Occupational fatigue is a primary factor leading to work-related musculoskeletal disorders (WRMSDs). Kinematic and kinetic experimental studies have been able to identify indicators of WRMSD, but research addressing real-world workplace scenarios is lacking. Hence, the authors of this study aimed to [...] Read more.
(1) Background: Occupational fatigue is a primary factor leading to work-related musculoskeletal disorders (WRMSDs). Kinematic and kinetic experimental studies have been able to identify indicators of WRMSD, but research addressing real-world workplace scenarios is lacking. Hence, the authors of this study aimed to assess the influence of physical strain on the Borg CR-10 body map, ergonomic risk scores, and foot pressure in a real-world setting. (2) Methods: Twenty-four participants (seventeen men and seven women) were included in this field study. Inertial measurement units (IMUs) (n = 24) and in-shoe plantar pressure measurements (n = 18) captured the workload of production and office workers at the beginning of their work shift and three hours later, working without any break. In addition to the two 12 min motion capture processes, a Borg CR-10 body map and fatigue visual analog scale (VAS) were applied twice. Kinematic and kinetic data were processed using MATLAB and SPSS software, resulting in scores representing the relative distribution of the Rapid Upper Limb Assessment (RULA) and Computer-Assisted Recording and Long-Term Analysis of Musculoskeletal Load (CUELA), and in-shoe plantar pressure. (3) Results: Significant differences were observed between the two measurement times of physical exertion and fatigue, but not for ergonomic risk scores. Contrary to the hypothesis of the authors, there were no significant differences between the in-shoe plantar pressures. Significant differences were observed between the dominant and non-dominant sides for all kinetic variables. (4) Conclusions: The posture scores of RULA and CUELA and in-shoe plantar pressure side differences were a valuable basis for adapting one-sided requirements in the work process of the workers. Traditional observational methods must be adapted more sensitively to detect kinematic deviations at work. The results of this field study enhance our knowledge about the use and benefits of sensors for ergonomic risk assessments and interventions. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Wearable Sensing Technologies for Biomechanical and Physiological Monitoring and Analysis)
►▼ Show Figures

Figure 1

16 pages, 317 KiB  
Article
Effects of Contextual Variables on Match Load in a Professional Soccer Team Attending to the Different Season Periods
by Rodrigo dos Santos Guimarães, Tomás García-Calvo, Javier Raya-González, José C. Ponce-Bordón, Pedro Fatela and David Lobo-Triviño
Sensors 2024, 24(2), 679; https://doi.org/10.3390/s24020679 - 21 Jan 2024
Viewed by 930
Abstract
This study aimed to analyze the effects of contextual variables (i.e., match location and match outcome) and season periods on match load (i.e., internal and external load) in professional Brazilian soccer players. Thirty-six professional players from the same soccer team participated in this [...] Read more.
This study aimed to analyze the effects of contextual variables (i.e., match location and match outcome) and season periods on match load (i.e., internal and external load) in professional Brazilian soccer players. Thirty-six professional players from the same soccer team participated in this study. The season was split into four phases: matches 1–16 (i.e., Phase 1 = P1); matches 17–32 (i.e., Phase 2 = P2); matches 33–48, (i.e., Phase 3 = P3); matches 49–65 (i.e., Phase 4 = P4). Considering match outcome, when the team wins, Cognitive load, Emotional load, and Affective load were significantly higher in away vs. home matches (p < 0.05). Considering season phases, in P3, Mental Fatigue was significantly higher in drawing than in losing matches (p < 0.05). Additionally, considering the match outcome, when the team lost, Total Distance (TD)/min and TD > 19 km·h−1/min were significantly lower in P1 than P2 (p < 0.001), P3 (p < 0.001), and P4 (p < 0.001). These results suggest to strength and conditioning coaches the need to consider the outcome and location of the previous game when planning the week, as well as the phase of the season they are in to reduce fatigue and injury risk. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Enhanced Wearable Sensing Technologies for Biomechanical and Physiological Monitoring and Analysis)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop