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Modeling study demonstrates prosthetic embodiment metrics in upper-limb prosthesis users, highlighting key behavioral predictors of device adoption.
Human performance models for upper-limb prostheses commonly predict task time, error, and cognitive workload but seldom quantify prosthetic embodiment: the experience that a device is part of the body and under voluntary control. We introduce HPM-UP2, an extension of HPM-UP, to connect usability modeling with embodiment and adoption. HPM-UP2 preserves HPM-UP outputs for learnability, errors, workload, task completion time, and satisfaction, and adds a bounded Prosthetic Embodiment Score (PES) combining workload, performance, and control instability across arm pose and days. We validate required observables using 3 open datasets: free-living bilateral actigraphy (20 prosthesis users, 20 intact adults), reach-to-grasp kinematics (6 participants, 1,260 trials), and EMG arm-translation data (2 participants, 720 trials). Prosthesis users showed high bimanual asymmetry and low coupling; kinematic workload scaled with object complexity; and EMG decoding revealed pose robustness but subject-dependent day drift.
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Choi et al. (2026) studied this question.
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