Subjects self-select forearm gestures identification based on post-processing and integrating sEMG sensors and AS

Abstract

The use of surface electromyography (sEMG) to determine movement intention has a lot of promise regarding artificial hand control and hemiplegia rehabilitation. Nevertheless, since sEMG is fragile and vulnerable to outside intervention, the current study focuses on identifying particular postures. When the subjects are swapped out, the recognition accuracy plummets. This study proposed a method in which the participant could select their forearm gestures on a voluntary basis. Nine subjects' selected movement datawere recorded using two sEMG and nine-axis attitude sensors. This paper used post-processing to optimize the features, which were then identified using K-Nearest Neighbor algorithms to increase recognition accuracy. The combination of the two, as well as post-processing, could improve the recognition effect with 96.2 ± 6.9% accuracy, which was a statistically significant difference from the other way. The proposed model couldbe employed as a user-independent movement classification, with these subjects being able to choose forearm movements independently. The proposed approach can potentially increase the adaptability of sEMG-based purpose recognition strategies and play a key role in popularizing manipulators or prosthetic control and recovery training.