发表机构
The University of Sydney; James Cook University; Nepean Hospital（悉尼大学; 詹姆斯·库克大学; 尼皮恩医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PhysioAI利用临床知识字典生成语义锚点，在训练时监督骨骼表示学习，提升物理治疗动作识别准确率，在多个基准上超越现有方法。
AI 中文摘要
基于骨骼的动作识别可以支持物理治疗练习的自动跟踪，特别是在持续面对面监督不可行的远程康复环境中。然而，大多数现有方法是为大规模日常动作基准开发的，而非康复场景。公开的康复练习数据集通常较小，且不同练习类别之间的运动学差异细微。对于有运动障碍的参与者，练习执行也可能在幅度、速度和协调性上偏离标准运动模式，增加了类内变异性，使得基于骨骼的模型更难进行可靠识别。我们提出PhysioAI，一种临床知识引导的语义监督框架，将结构化的物理治疗知识注入骨骼表示学习。PhysioAI结合了基于图的人体运动时空建模与从结构化临床知识字典（CKD）中导出的训练时语义锚点。CKD描述使用冻结的对比语言-图像预训练（CLIP）模型进行编码，并投影到锚点空间，在那里为骨骼表示学习提供类别特定的语义目标。由此产生的CKD派生锚点仅在骨骼模型训练期间使用；推理时仅需骨骼输入。在受试者不相交的评估下，PhysioAI在KiMoRe Overall上达到$99.03\pm1.34\\%$，在Hard-67压力测试上达到$94.64\pm7.36\\%$，在UI-PRMD Overall上达到$87.44\pm7.69\\%$。这些结果分别超过每个端点的最强比较器$0.27$、$2.87$和$1.33$个百分点（pp）。这些发现表明，结构化临床知识可以作为物理治疗动作识别训练时监督的有效来源。
英文摘要
Skeleton-based action recognition can support automated tracking of physiotherapy exercises, particularly in remote rehabilitation settings where continuous in-person supervision is impractical. However, most existing methods are developed for large-scale daily-action benchmarks rather than rehabilitation scenarios. Public rehabilitation exercise datasets are typically small, with only subtle kinematic differences between exercise classes. For participants with motor impairments, exercise execution may also deviate from standard movement patterns in amplitude, speed, and coordination, increasing intra-class variability and making reliable recognition more difficult for skeleton-based models. We propose PhysioAI, a clinical knowledge-guided semantic supervision framework that injects structured physiotherapy knowledge into skeleton representation learning. PhysioAI combines graph-based spatiotemporal modelling of human movement with training-time semantic anchors derived from a structured Clinical Knowledge Dictionary (CKD). The CKD descriptions are encoded using a frozen Contrastive Language-Image Pre-training (CLIP) model and projected into an anchor space, where they provide class-specific semantic targets for skeleton representation learning. The resulting CKD-derived anchors are used only during skeleton-model training; inference requires only skeleton inputs. Under subject-disjoint evaluation, PhysioAI achieves $99.03\pm1.34\%$ on KiMoRe Overall, $94.64\pm7.36\%$ on the Hard-67 stress test, and $87.44\pm7.69\%$ on UI-PRMD Overall. These results exceed the strongest comparator for each endpoint by $0.27$, $2.87$, and $1.33$ percentage points (pp), respectively. These findings demonstrate that structured clinical knowledge can serve as an effective source of training-time supervision for physiotherapy action recognition.
CommentsIncludes supplementary material and ancillary data files
