Simplifying the interaction between humans and computers has become intensively important. Handgesture 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 proposedto identify the redundant channels. It is found that there exist three common redundant channelsacross nine subjects., and all located at the inside of the forearm.
|Name||IEEE ICMLC Proceeding Series|
|Conference||2018 International Conference on Machine Learning and Cybernetics|
|Abbreviated title||ICMLC 2018|
|Period||15/07/18 → 18/07/18|
- Gesture Recognition