想用 AI 辅助康复评分又怕黑盒？PhaseAware 精度高（RMSE 降 88.9%），还能给出运动阶段和身体部位的敏感线索，方便医生审查边界案例。
PhaseAware 是一种结合时间骨干网络与阶段-身体组描述符的紧凑框架，用于连续康复质量评估。在 UI-PRMD 深蹲协议上，PhaseAware 的 RMSE 降至 0.0230，相比基线降低了 88.9%。在 KIMORE 深蹲子集上同样表现良好，表明阶段感知设计在相关协议间具有迁移性。除了评分预测，PhaseAware 还能基于阶段和身体层面的灵敏度生成结构化审查线索，突出对预测最重要的运动阶段和身体区域。该架构采用骨干条件门控残差机制稳定特征表示，支持在资源受限场景下运行。
PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring
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.