Previous research has shown an exposure-response relationship between repetitive forceful exertions and musculoskeletal disorders (MSDs) of the upper extremity. Sustained or repetitive non-neutral postures further increase this risk. Traditional methods for quantifying these exposures, such as direct measurements and detailed video analyses, are highly time-consuming, particularly for assessing HAL. To address this challenge, the present study investigates the potential of a wearable sEMG cuff as a more efficient tool for capturing hand exertions. sEMG data were recorded at 500 Hz using an 8-channel forearm cuff. Video analysis, captured from side and overhead views at 30 frames per second, served as the gold standard for comparison. Different analytical approaches were tested, including various MVC thresholds (5%, 10%, and 20%) and window sizes ranging from 0 to 2 seconds, comparing these to MVTA measurements. Results indicated that increasing the %MVC threshold and window size improved the accuracy of duty cycle measurements, with the best relative error of-5.37% observed at 20% MVC and a 2-second window size. Conversely, repetition rate errors were minimized with lower thresholds and smaller windows, achieving a-39.05% error at 20% MVC with no window size. Using the conjugate gradient method for optimization, the average relative error for the Duty Cycle was 8.52%, while the repetition rate showed a higher error of 175.56%. For HAL scores, the conjugate gradient method resulted in an average error of 96.17% and a median error of 47.13%. These results suggest that wearable sEMG technology can provide an effective and scalable alternative for quantifying biomechanical exposures, offering a promising solution for overcoming the limitations of traditional assessment methods.
Using a Wearable sEMG Forearm Cuff to Measure Hand Exertion Frequency and Duration in Hand-Intensive Jobs
Bendra, P. (Author). 2024
Student thesis: Master's Thesis