Abstract
The typical control of myoelectric interfaces, whether in laboratory settings or real-life prosthetic applications, largely relies on visual feedback because proprioceptive signals from the controlling muscles are either not available or very noisy. We conducted a set of experiments to test whether artificial proprioceptive feedback, delivered non-invasively to another limb, can improve control of a two-dimensional myoelectrically-controlled computer interface. In these experiments, participants’ were required to reach a target with a visual cursor that was controlled by electromyogram signals recorded from muscles of the left hand, while they were provided with an additional proprioceptive feedback on their right arm by moving it with a robotic manipulandum. Provision of additional artificial proprioceptive feedback improved the angular accuracy of their movements when compared to using visual feedback alone but did not increase the overall accuracy quantified with the average distance between the cursor and the target. The advantages conferred by proprioception were present only when the proprioceptive feedback had similar orientation to the visual feedback in the task space and not when it was mirrored, demonstrating the importance of congruency in feedback modalities for multi-sensory integration. Our results reveal the ability of the human motor system to learn new inter-limb sensory-motor associations; the motor system can utilize task-related sensory feedback, even when it is available on a limb distinct from the one being actuated. In addition, the proposed task structure provides a flexible test paradigm by which the effectiveness of various sensory feedback and multi-sensory integration for myoelectric prosthesis control can be evaluated.
Highlights
M YOELECTRIC interfaces use the electrical activity of muscles [electromyogram (EMG)] to control computers or electrically actuated devices, such as prosthetic limbs [1]
Our analysis focused on analyzing the subject's ability to learn the presence and absence of the visual feedback condition
Subjects were considered as nonlearners, if the average target mismatch (3) of all trials with visual feedback was greater than 0.8
Summary
M YOELECTRIC interfaces use the electrical activity of muscles [electromyogram (EMG)] to control computers or electrically actuated devices, such as prosthetic limbs [1]. While there have been several attempts to deliver sensory feedback about the state of the interface through grip force feedback via vibro-tactile, mechano-tactile, or electro-tactile stimulation [2]–[5], and feedback of the prosthetic joint angle or position through cutaneous stimuli [7], [8], it is not yet clear whether provision of other sensory signals in addition to vision would augment control of myoelectric interfaces. This is because, conventionally, the effectiveness of these sensory signals is quantified when vision is withheld. It was shown that additional proprioceptive feedback could improve control of a visual representation of the grasp, albeit only for small target sizes
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