Volitional control of upper-limb exoskeleton empowered by EMG sensors and machine learning computing

Processing multiple channels of bioelectrical signals for bionic assistive robot volitional motion control is still a challenging task due to the interference of systematic noise, artifacts, individual bio-variability, and other factors. Emerging machine learning (ML) provides an enabling technology for the development of the next generation of smart devices and assistive systems and edging computing. However, the integration of ML into a robotic control system faces major challenges. This paper presents ML computing to process twelve channels of shoulder and upper limb myoelectrical signals for shoulder motion pattern recognition and real-time upper arm exoskeleton volitional control. Shoulder motion patterns included drinking, opening a door, abducting, and resting. ML algorithms included support vector machine (SVM), artificial neural network (ANN), and Logistic regression (LR). The accuracy of the three ML algorithms was evaluated respectively and compared to determine the optimal ML algorithm. Results showed that overall SVM algorithms yielded better accuracy than the LR and ANN algorithms. The offline accuracy was 96 +/- 3.8% for SVM, 96 +/- 3.8% for ANN, and 93 +/- 6.3% for LR, while the online accuracy was 90 +/- 9.1% for SVM, 86 +/- 12.0% for ANN, and 85 +/- 11.3% for LR respectively. The offline pattern recognition had a higher accuracy than the accuracy of real-time exoskeleton motion control. This study demonstrated that ML computing provides a reliable approach for shoulder motion pattern recognition and real-time exoskeleton volitional motion control.