基于人体动作反馈的上肢康复机器人主动感知系统

发布时间：2024-12-14 12:26

摘要: 提出了基于人体动作命令反馈的主动康复训练方法．首先，对采集到的4通道上肢肌电信号进行小波包分解，提取小波系数的log特征，输入到神经网络辨识，对上肢的8种常用动作取得了92.86%的辨识成功率．随后，将辨识结果动作以虚拟动画的形式展示给患者，患者针对辨识动作进行点头确认或者摇头拒绝．采用Kinect获取患者的动作视频，经过人脸辨识缩小检测范围，利用光流特征与先验经验结合，对比分析了投票法、Sigmoid法与tanh法的动作辨识结果，在人为干扰的条件下，Sigmoid法仍然表现优良．设计了Sigmoid法的参数修正方法，最终准确度达87.25%，无危险性误判率为12.75%．经过主动康复实验，本文提出的基于人体动作命令反馈的主动康复训练意图感知方法具有良好效果，适用于主动康复训练场合．

Abstract: An active rehabilitation training method based on the feedback of human action command is proposed. Firstly, the 4 channels of upper limb electromyogram (EMG) signals are decomposed by wavelet packet. The log features of wavelet coefficients are extracted and input into neural network for identification. The recognition accuracy of 8 common movements of the upper limb is 95.8%. Subsequently, the action of identification result is displayed to the patient with a virtual animation, and the patient nods head to accept the identification action or shakes the head to reject it. Kinect is used to get the patient movement video, and the detection range is narrowed through face recognition. Combining the characteristics of light flow with the prior experience, the action recognition results of the voting method, the Sigmoid method and the tanh method are compared and analyzed. When adding jamming artificially, the Sigmoid method still gets a good result. And then the parameters of the Sigmoid method are corrected, which leads to the accuracy of 87.25% and the no-hazard misjudgement of 12.75%. In the active rehabilitation experiment, the proposed active rehabilitation intention perception method based on human action command feedback shows fine effect, which indicates it is suitable for active rehabilitation training.

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