Abstract
Measuring muscle fatigue is one essential and standard method to quantify the ergonomic risks associated with prolonged low-load exposure. However, measuring muscle fatigue using EMG-based methods has shown conflicting results under low-load but sustained work conditions, e.g., prolonged sitting. Muscle stimulation technology provides an alternative way to estimate muscle fatigue development during such work conditions by monitoring the stimulation-evoked muscle responses, which, however, could be restricted by the accessibility and measurability of targeted muscles. This study proposes a computer vision-based method to overcome such potential restrictions by visually quantifying the muscle belly displacement caused by muscle stimulation. The results demonstrate the ability of the developed computer vision-based stimulation method to detect muscle fatigue from prolonged low-load tasks. Current results can be used as a foundation to develop a sensitive and reliable method to quantify the adverse effects of the daily low-load sustained condition in occupational and nonoccupational settings.
Highlights
The goal of this research was to develop a reliable computer vision-based method to quantify potential muscle fatigue resulting from prolonged low-load exposure
In order to detect potential muscle fatigue, displacements measured from 10 s of video from before and after trialsmuscles were compared, and the corresponding changes were
Muscle fatigue was detected if protocol, and corresponding stimulation responses were successfully estimated by capthe overall magnitudes of displacements measured after the fatiguing trial were smaller turing the RoI feature displacements, which were further used to detect potential muscle than those measured the fatiguing trial.participant, feature displacement from each fatigue caused by thebefore fatiguing trial
Summary
The levels of muscle contractions are low (i.e., 40 min) [5,6,7,8,9,10,11]
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