This proof-of-concept study demonstrates how EMG-based assessment tools improve risk estimates for distal upper extremity disorders, highlighting their potential over traditional observation methods.
Work-related distal upper extremity (DUE) disorders are common in hand-intensive occupations due to cumulative strain. Existing assessment tools rely on subjective ratings and often fail to capture dynamic workload over varied work schedules. This proof-of-concept study evaluated a surface electromyography (EMG)-based version of the Distal Upper Extremity Tool (EMG-DUET), designed to provide objective and task-flexible estimates of cumulative strain and risk. Nineteen participants were video recorded during job tasks while forearm EMG was collected, yielding 78 manual tasks for analysis. Risk estimates from EMG-DUET were compared with those from forearm EMG peak values (EMG-Peak), visual observation-based DUET (OBS-DUET), the ACGIH Hand Activity Level Threshold Limit Value (OBS-HATLV), and prevalence estimates of DUE symptoms. Both DUET variants aligned most closely with their input-matched references, and EMG-based tools showed stronger, though modest, correlations with symptom prevalence than observation-based tools. EMG-DUET demonstrates promise, but refinement and validation in full-shift, prospective studies are needed.
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Fan et al. (2025) studied this question.
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