Integrating computer vision to prosthetic hand control with sEMG: Preliminary results in grasp classification - Wang, Shuo; Zheng, Jingjing; Huang, Ziwei; Zhang, Xiaoqin; Prado da Fonseca, Vinicius; Zheng, Bin; Jiang, Xianta (Frontiers in Robotics and AI, 2022)
Myoelectric prosthesis is a promising tool to restore the hands abilities of amputees, but the classification accuracy of sEMG is not high enough for real-time application. Researchers proposed of integrating sEMG signals with another feature which is not affected by amputation. The strong coordination between vision and hand manipulation makes us consider of including the visual information into prosthetic hand control. In this study, we identifies 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 encourage us to further explore the methods for incorporating computer vision to myoelectric data to enhancing the movement control of prosthetic hands.