Repetitive workplace tasks are associated with fatigue-induced changes to shoulder muscular strategies, potentially altering kinematics and elevating susceptibility to tissue overexposures. Accessible and reliable methods to detect shoulder muscle fatigue in the workplace are therefore valuable. Detectable changes in joint motion may provide a plausible fatigue identification method. In this investigation, the onset of the first kinematic changes, as identified by a symbolic motion representation (SMSR) algorithm, and the onset of substantial surface electromyography (sEMG) mean power frequency (MPF) fatigue were not significantly different, both occurring around 10% of task duration. This highlights the potential utility of SMSR identified directional changes in joint motion during repetitive tasks as a cue of substantial muscle fatigue, enabling ergonomics responses that can mitigate shoulder muscular fatigue accumulation and its associated deleterious physical effects. Practitioner Summary: The onset of substantial muscle fatigue during a repetitive dynamic task was assessed using kinematics and myoelectric-based techniques. Algorithmically detectable directional changes in upper extremity joint motion occurred with the onset of substantial muscle fatigue, highlighting the potential of this as a useful approach for workplace fatigue identification.

Efficient and holistic identification of fatigue-induced movement strategies can be limited by large between-subject variability in descriptors of joint angle data. One promising alternative to traditional, or computationally intensive methods is the symbolic motion structure representation algorithm (SMSR), which identifies the basic spatial-temporal structure of joint angle data using string descriptors of temporal joint angle trajectories. This study attempted to use the SMSR to identify changes in upper extremity time series joint angle data during a repetitive goal directed task causing muscle fatigue. Twenty-eight participants (15 M, 13 F) performed a seated repetitive task until fatigued. Upper extremity joint angles were extracted from motion capture for representative task cycles. SMSRs, averages and ranges of several joint angles were compared at the start and end of the repetitive task to identify kinematic changes with fatigue. At the group level, significant increases in the range of all joint angle data existed with large between-subject variability that posed a challenge to the interpretation of these fatigue-related changes. However, changes in the SMSRs across participants effectively summarized the adoption of adaptive movement strategies. This establishes SMSR as a viable, logical, and sensitive method of fatigue identification via kinematic changes, with novel application and pragmatism for visual assessment of fatigue development.