The arXiv preprint proposes matching real upper-limb motion against a physics-based musculoskeletal model to identify which muscle group is fatigued, offering a contactless alternative to hands-on clinical exams. Methodology relies on extracting diagnostic features from both captured free-space movements and simulated fatigue conditions, yet the submission provides no participant count, demographic details, or statistical power analysis, limiting claims of reliability. As a 2026 preprint it remains unpeer-reviewed. The core insight missed by the authors is immediate applicability to ergonomics: embedding such simulation loops into wearable or camera-based systems could flag fatigue onset during repetitive factory tasks or gym routines before tissue damage accumulates. Related work on OpenSim-driven fatigue modeling (Steele et al., 2017, Journal of Biomechanics) and real-time inverse-dynamics pipelines (Delp et al., 2007, IEEE TBME) shows that sim-to-real gaps narrow when muscle-tendon parameters are calibrated per user, an adjustment the current paper under-explores. Limitations include dependence on accurate motion capture and the absence of testing under varying loads or postures typical of industrial settings. Synthesizing these threads reveals a practical pathway: real-time fatigue maps could feed directly into exoskeleton controllers or workstation adjustments, cutting repetitive-strain injuries whose annual U.S. costs exceed $20 billion.