Surface EMG electrode distribution for thumb motion classification based on wireless communication equipment

The interaction between humans and computers has become more necessary and more specific. Thumb as the most important finger plays a decisive role in decoding the gesture, especially in controlling smart phones and many other smart devices, etc. As a result, this study aims to decode the different thumb gestures from sEMG signal and to improve the robustness of gesture recognition and decrease the influence of physiological conditions and the electrode displacement between different users. In this paper, we use the Bluetooth wireless communication and focus on the relationship between the EMG signal and the electrode identifier number. We change the electrode’s number into a new feature, and combining the traditional features with the new features to verify the electrode’s number has a correlation with the thumb gesture. Experiments show that after adding new features, the gesture recognition rate has increased.