基于姿态估计与GRU网络的人体康复动作识别

康复锻炼是脑卒中患者的重要治疗方式,为提高康复动作识别的准确率与实时性,更好地辅助患者在居家环境中进行长期康复训练,结合姿态估计与门控循环单元(GRU)网络提出一种人体康复动作识别算法Pose-AMGRU。采用OpenPose姿态估计方法从视频帧中提取骨架关节点,经过姿态数据预处理后得到表达肢体运动的关键动作特征,并利用注意力机制构建融合三层时序特征的GRU网络实现人体康复动作分类。实验结果表明,该算法在KTH和康复动作数据集中的识别准确率分别为98.14%和100%,且在GTX1060显卡上的运行速度达到14.23frame/s,具有较高的识别准确率与实时性。

Human Rehabilitation Action Recognition Based on Pose Estimation and GRU Network

Rehabilitation exercise is an important treatment method for stroke patients.This paper proposes a rehabilitation action recognition algorithm,Pose-AMGRU,which combines pose estimation with Gated Recurrent Unit(GRU)in order to improve the accuracy and real-time performance of rehabilitation action recognition,and thus assist patients in in-home long-term rehabilitation training.The algorithm uses OpenPose pose estimation method to extract the skeleton joints from video frames,and the pose data is preprocessed to obtain the key action features that represent body movement.Then a GRU network with three-layer time series features is constructed by using the attention mechanism to realize rehabilitation action classification.Experimental results on KTH dataset and rehabilitation action dataset show that the proposed algorithm increases the recognition accuracy to 98.14%and 100%,and its running speed on GTX1060 reaches 14.23 frame/s,which demonstrates its excellent recognition accuracy and real-time performance.