Semi-autonomous control of prosthetic hands based on multimodal sensing, human grasp demonstration and user intention

Semi-autonomous control strategies for prosthetic hands provide a promising way to simplify and improve the grasping process for the user by adopting techniques usually applied in robotic grasping. Such strategies endow prosthetic hands with the ability to autonomously select and execute grasps while keeping the user in the loop to intervene at any time for triggering, accepting or rejecting decisions taken by the controller in an intuitive and easy way. In this paper, we present a semi-autonomous control strategy that allows the user to perform fluent grasping of everyday objects based on a single EMG channel and a multi-modal sensor system embedded in the hand for object perception and autonomous grasp execution. We conduct a user study with 20 subjects to assess the effectiveness and intuitiveness of our semi-autonomous control strategy and compare it to a conventional electromyography-based control strategy. The results show that the workload is reduced by 25.9 % compared to conventional electromyographic control, the physical demand is reduced by 60 % and the grasping process is accelerated by 19.4 %.