🤖 AI Summary
To address the challenge of accurately identifying compensatory movements during stroke rehabilitation, this paper proposes a Graph Convolutional Network–Long Short-Term Memory–Attention (GCN-LSTM-ATT) fusion model. Leveraging skeletal sequence data captured by Kinect, the model employs GCN to encode spatial topological relationships among joints, LSTM to capture temporal dynamics, and an attention mechanism to enhance discriminative capability for critical motion segments. Ablation studies confirm the effectiveness of each component. Evaluated on a real-world stroke rehabilitation dataset, the model achieves an accuracy of 0.8580—significantly outperforming conventional methods including SVM, KNN, and Random Forest. This work establishes a novel paradigm for automated, fine-grained identification of compensatory motions, thereby enabling data-driven personalization and optimization of rehabilitation strategies.
📝 Abstract
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.