用手机实时识别和评估理疗动作，帮患者练得准、练得对。
An M-Health Algorithmic Approach to Identify and Assess Physiotherapy Exercises in Real Time
- 把动作拆成静态姿势，通过摄像头和神经网络提取身体关键点
- 用轻量模型分类姿势并评分，动态匹配动作序列发现偏差
- 全程本地运行，适合远程康复监督，无需云端计算
本文提出一种高效的算法框架，利用移动设备实现实时识别、分类与评估人体理疗动作。该方法将运动视为一系列静态姿态的序列，通过姿态估计神经网络从摄像头输入中估算身体关键点，并将其转换为基于三角函数的角度特征，再由轻量级监督模型进行分类，生成帧级姿态预测与准确度评分。为识别完整动作并检测与标准模式的偏差，采用基于改进莱文施泰因距离的动态规划方案，实现鲁棒的序列匹配与误差定位。系统完全在客户端运行，保障可扩展性与实时性能。实验验证了该方法的有效性，凸显其在远程理疗监督与m-health应用中的适用性。
原文摘要 · Abstract (English)
This work presents an efficient algorithmic framework for real-time identification, classification, and evaluation of human physiotherapy exercises using mobile devices. The proposed method interprets a kinetic movement as a sequence of static poses, which are estimated from camera input using a pose-estimation neural network. Extracted body keypoints are transformed into trigonometric angle-based features and classified with lightweight supervised models to generate frame-level pose predictions and accuracy scores. To recognize full exercise movements and detect deviations from prescribed patterns, we employ a dynamic-programming scheme based on a modified Levenshtein distance algorithm, enabling robust sequence matching and localization of inaccuracies. The system operates entirely on the client side, ensuring scalability and real-time performance. Experimental evaluation demonstrates the effectiveness of the methodology and highlights its applicability to remote physiotherapy supervision and m-health applications.
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