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Synergistic Orthopaedic Fatigue Tracking glove: SOFT glove - Preview

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

Authors

Ben Allen

Martin S Garrad

Carlos A Cifuentes



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
Deposit Date Jul 2, 2024
Public URL https://uwe-repository.worktribe.com/output/12040525