Estimation of Knee Joint Angle from Surface EMG Using Multiple Kernels Relevance Vector Regression

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 (circle) +/- 1.2(circle), RMSE of 4.81(circle) +/- 1.37(circle), and R-2 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.

Automated summary

In this paper, a knee joint angle estimation model based on the novel method of multiple kernel relevance vector regression (MKRVR) is proposed to improve the viability of human-robot interactive perception and reduce the complexity of the estimation model. By comparing with least squares support vector regression (LSSVR), the results show that MKRVR performs better in knee joint angle estimation.