Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification

作者：Shuo Wang, Jingjing Zheng, Ziwei Huang, Xiaoqin Zhang, Vinicius Prado da Fonseca, Bin Zheng, Xianta Jiang · 发表于：Frontiers in Robotics and AI · 年份：2022 · DOI：10.3389/frobt.2022.948238 · 被引用次数：14 · 研究领域：Muscle activation and electromyography studies、Neuroscience and Neural Engineering、EEG and Brain-Computer Interfaces

The myoelectric prosthesis is a promising tool to restore the hand abilities of amputees, but the classification accuracy of surface electromyography (sEMG) is not high enough for real-time application. Researchers proposed integrating sEMG signals with another feature that is not affected by amputation. The strong coordination between vision and hand manipulation makes us consider including visual information in prosthetic hand control. In this study, we identified a sweet period during the early reaching phase in which the vision data could yield a higher accuracy in classifying the grasp patterns. Moreover, the visual classification results from the sweet period could be naturally integrated with sEMG data collected during the grasp phase. After the integration, the accuracy of grasp classification increased from 85.5% (only sEMG) to 90.06% (integrated). Knowledge gained from this study encourages us to further explore the methods for incorporating computer vision into myoelectric data to enhance the movement control of prosthetic hands.