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.

翻译：本研究提出了一种高效的算法框架，用于利用移动设备实时识别、分类和评估人体物理治疗运动。该方法将动力学运动解释为一系列静态姿态序列，这些姿态通过姿态估计神经网络从摄像头输入中估计得出。提取的身体关键点被转换为基于三角角度的特征，并通过轻量级监督模型进行分类，以生成帧级姿态预测及准确度评分。为识别完整运动动作并检测其与规定模式的偏差，我们采用了一种基于改进Levenshtein距离算法的动态规划方案，实现了鲁棒的序列匹配及误差定位。该系统完全在客户端运行，确保了可扩展性与实时性能。实验评估验证了该方法的有效性，并突显了其在远程物理治疗监督和移动健康应用中的适用性。