Improved spatial–temporal graph convolutional networks for upper limb rehabilitation assessment based on precise posture measurement - Bai, Jing; Wang, Zhixian; Lu, Xuanming; Wen, Xiulan (Frontiers in Neuroscience, 2023)
After regular rehabilitation training, paralysis sequelae can be significantly reduced in patients with limb movement disorders caused by stroke. Rehabilitation assessment is the basis for the formulation of rehabilitation training programs and the objective standard for evaluating the effectiveness of training. However, the quantitative rehabilitation assessment is still in the experimental stage and has not been put into clinical practice. In this work, we propose improved spatial-temporal graph convolutional networks for upper limb rehabilitation assessment based on precise posture measurement. Two Azure Kinect are used to enlarge the angle range of the visual field and reduce occlusion during movement. The rigid body model of the upper limb with multiple degrees of freedom is established. And the inverse kinematics is optimized based on the hybrid particle swarm optimization algorithm, to reduce the biological inconsistency and improve the accuracy of upper limb posture measurement. The self-attention mechanism map is calculated to analyze the role of each upper limb joint in rehabilitation assessment, to improve the spatial-temporal graph convolution neural network model. Long short-term memory is built to explore the sequence dependence in spatial-temporal feature vectors, to improve the accuracy of rehabilitation assessment. An exercise protocol for detecting the distal reachable workspace and proximal self-care ability of the upper limb is designed, and a virtual environment is built to improve patients' initiative to participate in rehabilitation assessment. A platform is built for experiments, and the results show that the proposed method can effectively quantitatively evaluate the upper limb motor function of stroke patients.