Simultaneous estimation of hand joints’ angles towards sEMG-driven human-robot interaction
Simultaneous estimation of hand joints’ angles towards sEMG-driven human-robot interaction
复制标题
同时估计手关节角度以实现表面肌电驱动的人机交互
DOI：
10.1109/access.2022.3212046
复制
发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
张小栋
中科院分区：
文献类型：
--
作者：
王海;陶庆;苏娜;张小栋
人类有着灵巧的双手，可以帮助我们操作许多复杂的工具，但目前的研究大多集中在手势识别上，这就导致了假手缺乏连续、自然的运动。本文提出了一种基于多路表面肌电信号的手指运动连续同步解码方法，该算法可用于假手表面肌电信号解码控制、上肢康复外骨骼机器人和远程机械手控制。首先，采用基于滑动时间窗的特征提取器，从前臂8个通道的表面肌电信号中提取8种表面肌电信号特征（绝对均值、积分值、均方根、波形长度、对数特征、过零点和斜率符号变化）。将波动较大的掌指关节角度估计值输入深林回归模型进行优化，然后利用人工神经网络对该角度估计值进行优化，建立深林回归模型与人工神经网络相结合的综合回归模型;最后，利用综合回归模型对采集到的表面肌电信号进行连续精确解码，得到假手手指关节角度控制，值，其他指关节角度可以通过比例控制原理获得。实验结果表明，该方法的平均轨迹跟踪精度比传统的高斯过程方法提高了42%，达到了84.4%，证明了该方法对基于表面肌电信号的手指关节角度估计具有很好的效果。
Human beings havevery dexteroushands which help us manipulate a lot of complicated tools.But most researches focus on gestures’ recognition.This situation leads to thephenomenonthat our prosthetic hands lackcontinuous and natural movement. This paper proposes a method aimed to decode fingermovements continuously and simultaneously based on multi-channel surface electromyography signals.This algorithmis useful forsurfaceelectromyography(sEMG)decoding controlled for a prosthetic hand,exoskeleton robots for upper-limb rehabilitation, and remote mechanical hand control. Firstly, the feature extractor based on a sliding time window is usedto extract 8kinds of sEMG features (mean absolute value, integral sEMG value, root mean square, waveform length, logarithmic feature, zero-crossing points, and slope symbol change) from the sEMG signals of 8 channels of the forearm. The estimated angle of the metacarpophalangeal joint with large fluctuation is optimizedby inputtingthem tothe deep forest regression model; Then, the artificial neural network is used to optimize this estimated angle, to create a comprehensive regression model combining the deep forest regression model and artificial neural network; Finally, the comprehensive regression model is used to continuously and accurately decode the collected surface EMG signal to obtain the prosthetic hand finger joint angle control values, and the other finger joint angles can be obtained through proportional controlprinciple. The experimental results show that the average trajectory tracking accuracy of the proposed method is 42% higher than that of the traditional Gaussian process method, reaching 84.4%, which proves that the proposed method has a very good effect on finger joint angle estimation based on sEMG signa.