sEMG-based estimation of human arm force using regression model

The force estimation of human arm is one of the main problems while controlling biomechantronic system. It's an effective method to estimate the endpoint force of human arm through the surface electromyography (sEMG) produced by the contraction of the muscles. In this paper, sEMG-based force is estimated by both Bayesian Linear Regression (BLR) and Support Vector Regression (SVR) algorithms. Experimental results show that both models have high performances, with the average of RMS errors in BLR all below 2 N and in SVR mostly below 1 N. Meanwhile, the force estimated by the BLR model is highly linear correlated with the measured value. This paper also discusses the effect of force estimation when electrodes placed in different positions on arm. The performance is better while electrodes are placed on both forearm and upper arm.

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