School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Zhiyuan Research Institute, Hangzhou 310013, China.
Sensors (Basel). 2023 May 20;23(10):4934. doi: 10.3390/s23104934.
In wearable robots, the application of surface electromyography (sEMG) signals in motion intention recognition is a hot research issue. To improve the viability of human-robot interactive perception and to reduce the complexity of the knee joint angle estimation model, this paper proposed an estimation model for knee joint angle based on the novel method of multiple kernel relevance vector regression (MKRVR) through offline learning. The root mean square error, mean absolute error, and R2_score are used as performance indicators. By comparing the estimation model of MKRVR and least squares support vector regression (LSSVR), the MKRVR performs better on the estimation of the knee joint angle. The results showed that the MKRVR can estimate the knee joint angle with a continuous global MAE of 3.27° ± 1.2°, RMSE of 4.81° ± 1.37°, and R of 0.8946 ± 0.07. Therefore, we concluded that the MKRVR for the estimation of the knee joint angle from sEMG is viable and could be used for motion analysis and the application of recognition of the wearer's motion intentions in human-robot collaboration control.
在可穿戴机器人中，表面肌电信号（sEMG）在运动意图识别中的应用是一个热门研究课题。为了提高人机交互感知的可行性，并降低膝关节角度估计模型的复杂性，本文提出了一种基于多核相关向量回归（MKRVR）的新型方法的膝关节角度估计模型，通过离线学习实现。使用均方根误差、平均绝对误差和 R2 分数作为性能指标。通过比较 MKRVR 和最小二乘支持向量回归（LSSVR）的估计模型，MKRVR 在膝关节角度的估计中表现更好。结果表明，MKRVR 可以以连续全局 MAE 为 3.27°±1.2°、RMSE 为 4.81°±1.37°和 R 为 0.8946±0.07 的精度来估计膝关节角度。因此，我们得出结论，基于 sEMG 的 MKRVR 进行膝关节角度估计是可行的，可用于运动分析和识别穿戴者在人机协作控制中的运动意图。
