Shoulder Motion Intention Detection Through Myoelectric Pattern Recognition

In this letter, a motion intention detection (MID) problem from surface electromyographic (sEMG) signals, involving upper limbs, was faced through a pattern recognition approach. Linear discriminant analysis (LDA) and multinomial logistic regression (MLR) were used to tackle a multiclass classification for eight healthy subjects. The sEMG signals were segmented with a window centered on movement onset. Different feature sets, i.e., engaging time domain (TD) and frequency domain (FD), were used to fit the models. Moreover, principal component analysis was employed to reduce the whole TD+FD space. In this case, both models performed satisfactorily, reaching mean accuracy of 88.8 (LDA) and 91.8% (MLR). Finally, a heuristic method is proposed to evaluate feature importance. The results presented here support the use of pattern recognition control to solve MID problems, highlighting the possibility to integrate FD features to the commonly used TD ones, as in other myoelectric pattern recognition problems, e.g., hand gesture recognition.

Automated summary

This study addressed the motion intention detection problem from sEMG signals in upper limbs using pattern recognition approaches, achieving high accuracy with LDA and MLR. The integration of TD and FD features was effective in improving classification performance, supporting the use of pattern recognition for solving MID problems.