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Applied Sciences
Special Issues
State of the Art of Sensors in Biomechanics
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State of the Art of Sensors in Biomechanics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 3304

Special Issue Editors

Prof. Dr. Roy Cheung

E-Mail Website
Guest Editor
School of Health Sciences, Western Sydney University, Penrith, NSW 2751, Australia
Interests: gait biomechanics; wearable sensor; gait retraining
Prof. Dr. Chih-Hsiu Cheng

E-Mail Website
Guest Editor
School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
Interests: gait; walking speed; inertial sensors
Prof. Dr. Peter Shull

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: wearable systems using embedded electronics; real-time models; sensor fusion algorithms; novel feedback devices
Special Issues, Collections and Topics in MDPI journals
Dr. Asghar Rezaei

E-Mail Website
Guest Editor
Department of Physiology and Biomedical Engineering, Mayo Foundation for Medical Education and Research, Mayo Clinic, Rochester, MN 55905, USA
Interests: mechanical properties; biomechanics; mechanics of materials; finite element modeling; critical blow force; spine; expandable cage; kinematic testing; minimally invasive surgery; bone fracture; cervical spine

Special Issue Information

Dear Colleagues,

Recent technological advancements allow for out-of-lab measurements of human kinematics, kinetics, physiology, behaviour and performance, which maximise the research impact with real-world data. This Special Issue, therefore, aims to collect original research articles and review articles that discuss innovative methodologies for sensor applications in human biomechanics. Specifically, the issue will publish studies focused on the use of wearable sensors and smartphone applications, in combination with advance mathematical modelling, machine-learning algorithms and measurements, in order to highlight how innovative methodologies can enhance biomechanics analysis. Potential topics include, but are not limited to:

  • Design, development and validation of wearable sensors for biomechanics applications;
  • Mathematical modelling and machine learning algorithms for medical, healthcare and sports applications;
  • New methodologies to promote user friendliness and usability of wearable sensors.

Prof. Dr. Roy Cheung
Prof. Dr. Chih-Hsiu Cheng
Prof. Dr. Peter Shull
Dr. Asghar Rezaei
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. Applied Sciences 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 2400 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 sensors
  • smartphone
  • kinematics
  • kinetics
  • performance
  • behaviour

Published Papers (1 paper)

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Research

10 pages, 1518 KiB  
Article
Skill Level Classification in Basketball Free-Throws Using a Single Inertial Sensor
by Xiaoyu Guo, Ellyn Brown, Peter P. K. Chan, Rosa H. M. Chan and Roy T. H. Cheung
Appl. Sci. 2023, 13(9), 5401; https://doi.org/10.3390/app13095401 - 26 Apr 2023
Cited by 1 | Viewed by 1717
Abstract
Wearable sensors are an emerging technology, with growing evidence supporting their application in sport performance enhancement. This study utilized data collected from a tri-axial inertial sensor on the wrist of ten recreational and eight professional basketball players while they performed free-throws, to classify [...] Read more.
Wearable sensors are an emerging technology, with growing evidence supporting their application in sport performance enhancement. This study utilized data collected from a tri-axial inertial sensor on the wrist of ten recreational and eight professional basketball players while they performed free-throws, to classify their skill levels. We employed a fully connected convolutional neural network (CNN) for the classification task, using 64% of the data for training, 16% for validation, and the remaining 20% for testing the model’s performance. In the case of considering a single parameter from the inertial sensor, the most accurate individual components were upward acceleration (AX), with an accuracy of 82% (sensitivity = 0.79; specificity = 0.84), forward acceleration (AZ), with an accuracy of 80% (sensitivity = 0.78; specificity = 0.83), and wrist angular velocity in the sagittal plane (GY), with an accuracy of 77% (sensitivity = 0.73; specificity = 0.79). The highest accuracy of the classification was achieved when these CNN inputs utilized a stack-up matrix of these three axes, resulting in an accuracy of 88% (sensitivity = 0.87, specificity = 0.90). Applying the CNN to data from a single wearable sensor successfully classified basketball players as recreational or professional with an accuracy of up to 88%. This study represents a step towards the development of a biofeedback device to improve free-throw shooting technique. Full article
(This article belongs to the Special Issue State of the Art of Sensors in Biomechanics)
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