用骨骼数据检测中风后代偿性动作，准确率达85.8%。
Graph Convolutional Long Short-Term Memory Attention Network for Post-Stroke Compensatory Movement Detection Based on Skeleton Data
- 结合图卷积与注意力机制的LSTM模型，捕捉动作时序与关节关系。
- 在16名患者数据上达到85.8%检测准确率，显著优于传统算法。
- 适合康复医学研究者及智能康复系统开发者参考。
多数中风患者存在上肢运动功能障碍。康复训练中代偿性动作普遍存在，不利于长期恢复。本研究提出基于骨骼数据的图卷积长短期记忆注意力网络（GCN-LSTM-ATT），用于中风后代偿性动作检测。选取16名中风患者，利用Kinect深度相机采集其执行特定康复动作的骨骼数据。经数据处理后，分别构建GCN-LSTM-ATT、支持向量机（SVM）、K近邻（KNN）和随机森林（RF）检测模型。结果显示，GCN-LSTM-ATT模型检测准确率达0.8580，显著高于传统机器学习算法。消融实验表明，模型各组件均对性能提升有显著贡献。该方法为中风后代偿性动作检测提供更精准有力工具，有助于优化患者康复训练策略。
原文摘要 · Abstract (English)
Most stroke patients experience upper limb motor dysfunction. Compensatory movements are prevalent during rehabilitation training, which is detrimental to patients' long-term recovery. Therefore, detecting compensatory movements is of great significance. In this study, a Graph Convolutional Long Short-Term Memory Attention Network (GCN-LSTM-ATT) based on skeleton data is proposed for the detection of compensatory movements after stroke. Sixteen stroke patients were selected in the research. The skeleton data of the patients performing specific rehabilitation movements were collected using the Kinect depth camera. After data processing, detection models were constructed respectively using the GCN-LSTM-ATT model, the Support Vector Machine(SVM), the K-Nearest Neighbor algorithm(KNN), and the Random Forest(RF). The results show that the detection accuracy of the GCN-LSTM-ATT model reaches 0.8580, which is significantly higher than that of traditional machine learning algorithms. Ablation experiments indicate that each component of the model contributes significantly to the performance improvement. These findings provide a more precise and powerful tool for the detection of compensatory movements after stroke, and are expected to facilitate the optimization of rehabilitation training strategies for stroke patients.
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