PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring

• URL: http://arxiv.org/abs/2607.20237v1
• Date: Wed, 22 Jul 2026 14:57:42 GMT
• Title: PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring
• Abstract summary: This study presents a compact framework for continuous rehabilitation quality assessment that combines a temporal backbone with phase- and body-group descriptors.<n>PhaseAware achieved an RMSE of 0.0230, corresponding to an 88.9% reduction relative to the accepted baseline.<n>It also maintained favorable performance on KIMORE, suggesting that the phase-aware design transfers across related squatting protocols.
• Score: 18.918947215697205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rehabilitation scoring systems are most useful when their outputs can be reviewed and interpreted within clinical workflows. This study presents PhaseAware, a compact framework for continuous rehabilitation quality assessment that combines a temporal backbone with phase- and body-group descriptors through a backbone-conditioned gated residual pathway. The model was evaluated on the UI-PRMD deep-squat protocol and further tested on the KIMORE squatting subset. On UI-PRMD, PhaseAware achieved an RMSE of 0.0230, corresponding to an 88.9% reduction relative to the accepted baseline. It also maintained favorable performance on KIMORE, suggesting that the phase-aware design transfers across related squatting protocols. In addition to score prediction, PhaseAware generates structured review cues based on phase- and body-level sensitivity, highlighting the movement stages and body regions most relevant to each prediction. The architecture employs a backbone-conditioned gated residual mechanism to stabilize feature representation, supporting use in resource-constrained settings. These cues are intended to support clinician review, boundary-case monitoring, and human-in-the-loop triage rather than autonomous decision-making. Overall, PhaseAware offers a practical and interpretable approach to rehabilitation scoring that may help integrate automated assessment into information systems while preserving clinician oversight.

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