Estimation of Maximum Shoulder and Elbow Joint Torques Based on Demographics and Anthropometrics

Abstract

Repetitive movements that involve a significant shift of the body's center of mass can lead to shoulder and elbow fatigue, which are linked to injury and musculoskeletal disorders if not addressed in time. Research has been conducted on the joint torque individuals can produce, a quantity that indicates the ability of the person to carry out such repetitive movements. Most of the studies surround gait analysis, rehabilitation, the assessment of athletic performance, and robotics. The aim of this study is to develop a model that estimates the maximum shoulder and elbow joint torque an individual can produce based on anthropometrics and demographics without taking a manual measurement with a force gauge (dynamometer). Nineteen subjects took part in the study which recorded maximum shoulder and elbow joint torques using a dynamometer. Sex, age, body composition parameters, and anthropometric data were recorded, and relevant parameters which significantly contributed to joint torque were identified using regression techniques. Of the parameters measured, body mass index and upper forearm volume predominantly contribute to maximum torque for shoulder and elbow joints; coefficient of determination values were between 0.6 and 0.7 for the independent variables and were significant for maximum shoulder joint torque (P<0.001) and maximum elbow joint torque (P<0.005) models. Two expressions illustrated the impact of the relevant independent variables on maximum shoulder joint torque and maximum elbow joint torque, using multiple linear regression. Coefficient of determination values for the models were between 0.6 and 0.7. The models developed enable joint torque estimation for individuals using measurements that are quick and easy to acquire, without the use of a dynamometer. This information is useful for those employing joint torque data in biomechanics in the areas of health, rehabilitation, ergonomics, occupational safety, and robotics. Clinical Relevance - The rapid estimation of arm joint torque without the direct force measurement can help occupational safety with the prevention of injury and musculoskeletal disorders in several working scenarios.

Publication DOI: https://doi.org/10.1109/EMBC48229.2022.9870906
Divisions: College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > School of Engineering and Technology
Funding Information: M. Menolotto is with the Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland (e-mail: matteo.menolotto@tyndall.ie) B. O’Flynn is with the Tyndall National Institute, University College Cork, T12 R5CP Cork, Ireland (e-mail: brendan.
Additional Information: © 2022 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ Funding Information: 10.13039/501100001602-Science Foundation Ireland (Grant Number: 16/RC/3918 (CONFIRM),12/RC/2289-P2 (INSIGHT))
Event Title: 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Event Type: Other
Event Dates: 2022-07-11 - 2022-07-15
Uncontrolled Keywords: Signal Processing,Biomedical Engineering,Computer Vision and Pattern Recognition,Health Informatics
ISBN: 9781728127835, 9781728127828
Last Modified: 16 Dec 2024 09:13
Date Deposited: 10 Feb 2023 12:27
Full Text Link:
Related URLs: https://ieeexpl ... ocument/9870906 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2022-09-08
Published Online Date: 2022-09-08
Authors: O'sullivan, Patricia
Menolotto, Matteo
O'Flynn, Brendan
Komaris, Sokratis (ORCID Profile 0000-0003-4623-9060)

Download

[img]

Version: Published Version

License: Creative Commons Attribution

| Preview

Export / Share Citation


Statistics

Additional statistics for this record