Synergistic Orthopaedic Fatigue Tracking glove: SOFT glove

Allen, Ben; Garrad, Martin S; Cifuentes, Carlos A

Abstract

Repetitive strain injury (RSI) is a major health issue with 200,000 new cases being reported per year within the UK alone, and common symptoms include cramping, prolonged pain, stiffness and weakness. This is especially prevalent within electrical assembly jobs, with a 40.2% chance that a worker suffers from some form of upper limb injury before retirement. A significant RSI cause is continuing to work after the onset of muscular fatigue. Existing solutions only focus on post-injury rehabilitation or support. Here, we introduce the SOFT glove which is able to detect fatigue through resistive bend sensors mounted to key locations on the hand, enabling workers to be notified of fatigue and potentially preventing the development of RSI. The viability of this design was validated in both a controlled study and a live sorting task. The trained classifier detected fatigue within both scenarios, with a minimum average accuracy of 95.67% when trained on only 15 seconds of data for controlled movements and 96.01% for 3 minutes of training data for a real-world task. Therefore, the SOFT glove can confidently predict the main RSI warning sign for repetitive work, potentially reducing RSI in the workplace.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)
• Start Date: Sep 1, 2024
• End Date: Sep 4, 2024
• Acceptance Date: Apr 30, 2024
• Online Publication Date: Oct 23, 2024
• Publication Date: Oct 23, 2024
• Deposit Date: Jul 2, 2024
• Publicly Available Date: Oct 23, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Pages: 767-772
• Series ISSN: 2155-1782
• Book Title: 2024 10th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)
• DOI: https://doi.org/10.1109/BioRob60516.2024.10719854
• Public URL: https://uwe-repository.worktribe.com/output/12040525