No abstract

Simplifying the interaction between humans and computers has become intensively important. Hand gesture contains large amount information that can facilitate the communication among humans, and it can also be utilized to interact with external devices. As a result, this study aims to decode the different hand gestures from sEMG signal. The thumb plays the most important role in hand-based object manipulation, such as touch screen control for smart phones, for which many thumb-based hand involved. Therefore, studying the relationship between EMG signals and the thumb movement has certain value for the future human-computer interaction. In this paper, we focus on the identification of electrode position. The signal from which is not so related to the thumb movement, and thus these sEMG channels can be reduced. In the experiment, a 16-channels sleeve is utilized and a variance-based method was proposed to identify the redundant channels. It is found that there exist three common redundant channels across nine subjects., and all located at the inside of the forearm.

Abstract. EMG is a signal which could reflect the information of human body's movement in residual limb muscle group. As a source of information, it could control the bionic prosthesis to complete the multi-degree of freedom of action. EMG prosthesis controlled the bionic prosthesis through the body's own physiological signal. It is remarkable significant for the disabled users that if the bionic prosthesis could understand the movement of the disabled users will. The purpose of the EMG signal acquisition is driving a bionic hand which could help the hand disabilities to achieve grasping, pushing, taking, holding etc. EMG prosthetic is a complex system, which involving multidisciplinary disciplines. And this paper mainly introduces the method of EMG extraction.

The interaction between humans and computers has become more necessary and more specific, and the informatization of human hands has made the human-machine interaction based on gesture recognition more and more extensive. Thumb as the most important finger plays a decisive role in decoding the gesture, especially in controlling of the 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 article, 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.