Surface electromyography-based action recognition and manipulator control

Abstract

To improve the quality of lives of disabled people, the application of intelligent prosthesis was presented and investigated. In particular, surface Electromyography (sEMG) signals succeeded in controlling the manipulator in human-machine interface, due to the fact that EMG activity belongs to one of the most widely utilized biosignals and can reflect the straightforward motion intention of humans. However, the accuracy of real-time action recognition is usually low and there is usually obvious delay in a controlling manipulator, as a result of which the task of tracking human movement precisely, cannot be guaranteed. Therefore, this study proposes a method of action recognition and manipulator control. We built a multifunctional sEMG detection and action recognition system that integrated all discrete components. A biopotential measurement analog-to-digital converter with a high signal-noise rate (SNR) was chosen to ensure the high quality of the acquired sEMG signals. The acquired data were divided into sliding windows for processing in a shorter time. Mean Absolute Value (MAV), Waveform Length (WL), and Root Mean Square (RMS) were finally extracted and we found that compared to the Genetic-Algorithm-based Support Vector Machine (GA-SVM), the back propagation (BP) neural network performed better in joint action classification. The results showed that the average accuracy of judging the 5 actions (fist clenching, hand opening, wrist flexion, wrist extension, and calling me) was up to 93.2% and the response time was within 200 ms, which achieved a simultaneous control of the manipulator. Our work took into account the action recognition accuracy and real-time performance, and realized the sEMG-based manipulator control eventually, which made it easier for people with arm disabilities to communicate better with the outside world.