Bai Jing, Wang Zhixian, Lu Xuanming, Wen Xiulan
Industrial Technology Research Institute of Intelligent Equipment, Nanjing Institute of Technology, Nanjing, China.
Jiangsu Provincial Engineering Laboratory of Intelligent Manufacturing Equipment, Nanjing, China.
Front Neurosci. 2023 Jul 11;17:1219556. doi: 10.3389/fnins.2023.1219556. eCollection 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 based on precise posture measurement for upper limb rehabilitation assessment. Two Azure Kinect are used to enlarge the angle range of the visual field. 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. 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. 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. The experimental results indicate that the proposed posture measurement method can reduce position jumps caused by occlusion, improve measurement accuracy and stability, and increase Signal Noise Ratio. By comparing with other models, our rehabilitation assessment model achieved the lowest mean absolute deviation, root mean square error, and mean absolute percentage error. The proposed method can effectively quantitatively evaluate the upper limb motor function of stroke patients.
经过定期康复训练，中风所致肢体运动障碍患者的瘫痪后遗症可显著减轻。康复评估是制定康复训练计划的基础，也是评估训练效果的客观标准。然而，定量康复评估仍处于实验阶段，尚未应用于临床实践。在这项工作中，我们提出了基于精确姿势测量的改进型时空图卷积网络用于上肢康复评估。使用两台Azure Kinect扩大视野角度范围。建立了具有多个自由度的上肢刚体模型，并基于混合粒子群优化算法对逆运动学进行了优化。计算自注意力机制图以分析每个上肢关节在康复评估中的作用，从而改进时空图卷积神经网络模型。构建长短期记忆来探索时空特征向量中的序列依赖性。设计了一个用于检测上肢远端可达工作空间和近端自理能力的运动方案，并构建了一个虚拟环境。实验结果表明，所提出的姿势测量方法可以减少遮挡引起的位置跳跃，提高测量精度和稳定性，并增加信噪比。通过与其他模型比较，我们的康复评估模型实现了最低的平均绝对偏差、均方根误差和平均绝对百分比误差。所提出的方法可以有效地定量评估中风患者的上肢运动功能。
