Improving simultaneous and proportional control from EMG signals based on a Two-Stage Regression Structure

A two-stage regression structure is proposed in this paper to improve simultaneous and proportional control based on electromyography (EMG) signals. Instead of considering the conventional approach with a regression model per degree of freedom (DoF), the proposed method applies a regression model per direction of each DoF: the 1^st stage detects DoFs and then is used to select the direction models of the 2^nd stage. By using linear regression on the data from one healthy experienced participant for 2 DoFs of his wrist, the proposed structure was evaluated offline with cross-evaluation and online with a real-time control of a cursor to hit some targets. The evaluation results showed a clear improvement of the proposed method in terms of accuracy, ability to reach the boundaries, reaction speed, accurate control and reactive control compared to the conventional approach. The potential of this structure also lies in the fact that it can use different regression methods, work for 3 DoFs and more and use the 1^st stage knowledge to improve performance of the 2^nd stage.