PhaseAware：可解释的康复评分框架，结合边界监控与人类反馈
PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring
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.
This report is categorized under AITOP's "Models" desk. Key entities and technical topics under continuous tracking include PhaseAware, UI-PRMD, KIMORE, 康复评分.
Source attribution: curated from arXiv cs.LG (@Yankai Zheng, Yuhe Liu, Yuxin Ma, Tianci Xue, Jiayuan Tian, Yu Fu, Yuxuan Hu, Jianing Wang, Zichun Xiao, Junya Mu, Shaohui Ma).
