Graph Convolutional Long Short-Term Memory Attention Network for Post-Stroke Compensatory Movement Detection Based on Skeleton Data

TL;DR

This paper introduces a novel GCN-LSTM-ATT model that effectively detects compensatory movements in stroke patients using skeleton data, outperforming traditional machine learning methods and aiding rehabilitation strategies.

Contribution

The study develops a new GCN-LSTM-ATT model for post-stroke movement detection, demonstrating superior accuracy over traditional algorithms using skeleton data from Kinect.

Findings

Detection accuracy of 0.8580 with GCN-LSTM-ATT

Model components significantly improve performance

Provides a tool for optimizing stroke rehabilitation

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.