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Artificial proprioceptive feedback for myoelectric control.

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 noninvasively 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.

SUBMITTER: Pistohl T

PROVIDER: S-EPMC7610977 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTo effectively replace the human hand, a prosthesis should seamlessly respond to user intentions but also convey sensory information back to the user. Restoration of sensory feedback is rated highly by the prosthesis users, and feedback is critical for grasping in able-bodied subjects. Nonetheless, the benefits of feedback in prosthetics are still debated. The lack of consensus is likely due to the complex nature of sensory feedback during prosthesis control, so that its effectiveness depends on multiple factors (e.g., task complexity, user learning).MethodsWe evaluated the impact of these factors with a longitudinal assessment in six amputee subjects, using a clinical setup (socket, embedded control) and a range of tasks (box and blocks, block turn, clothespin and cups relocation). To provide feedback, we have proposed a novel vibrotactile stimulation scheme capable of transmitting multiple variables from a multifunction prosthesis. The subjects wore a bracelet with four by two uniformly placed vibro-tactors providing information on contact, prosthesis state (active function), and grasping force. The subjects also completed a questionnaire for the subjective evaluation of the feedback.ResultsThe tests demonstrated that feedback was beneficial only in the complex tasks (block turn, clothespin and cups relocation), and that the training had an important, task-dependent impact. In the clothespin relocation and block turn tasks, training allowed the subjects to establish successful feedforward control, and therefore, the feedback became redundant. In the cups relocation task, however, the subjects needed some training to learn how to properly exploit the feedback. The subjective evaluation of the feedback was consistently positive, regardless of the objective benefits. These results underline the multifaceted nature of closed-loop prosthesis control as, depending on the context, the same feedback interface can have different impact on performance. Finally, even if the closed-loop control does not improve the performance, it could be beneficial as it seems to improve the subjective experience.ConclusionsTherefore, in this study we demonstrate, for the first time, the relevance of an advanced, multi-variable feedback interface for dexterous, multi-functional prosthesis control in a clinically relevant setting.

Project description:Improving the function of prosthetic arms remains a challenge, because access to the neural-control information for the arm is lost during amputation. A surgical technique called targeted muscle reinnervation (TMR) transfers residual arm nerves to alternative muscle sites. After reinnervation, these target muscles produce electromyogram (EMG) signals on the surface of the skin that can be measured and used to control prosthetic arms.To assess the performance of patients with upper-limb amputation who had undergone TMR surgery, using a pattern-recognition algorithm to decode EMG signals and control prosthetic-arm motions.Study conducted between January 2007 and January 2008 at the Rehabilitation Institute of Chicago among 5 patients with shoulder-disarticulation or transhumeral amputations who underwent TMR surgery between February 2002 and October 2006 and 5 control participants without amputation. Surface EMG signals were recorded from all participants and decoded using a pattern-recognition algorithm. The decoding program controlled the movement of a virtual prosthetic arm. All participants were instructed to perform various arm movements, and their abilities to control the virtual prosthetic arm were measured. In addition, TMR patients used the same control system to operate advanced arm prosthesis prototypes.Performance metrics measured during virtual arm movements included motion selection time, motion completion time, and motion completion ("success") rate.The TMR patients were able to repeatedly perform 10 different elbow, wrist, and hand motions with the virtual prosthetic arm. For these patients, the mean motion selection and motion completion times for elbow and wrist movements were 0.22 seconds (SD, 0.06) and 1.29 seconds (SD, 0.15), respectively. These times were 0.06 seconds and 0.21 seconds longer than the mean times for control participants. For TMR patients, the mean motion selection and motion completion times for hand-grasp patterns were 0.38 seconds (SD, 0.12) and 1.54 seconds (SD, 0.27), respectively. These patients successfully completed a mean of 96.3% (SD, 3.8) of elbow and wrist movements and 86.9% (SD, 13.9) of hand movements within 5 seconds, compared with 100% (SD, 0) and 96.7% (SD, 4.7) completed by controls. Three of the patients were able to demonstrate the use of this control system in advanced prostheses, including motorized shoulders, elbows, wrists, and hands.These results suggest that reinnervated muscles can produce sufficient EMG information for real-time control of advanced artificial arms.

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Artificial proprioceptive feedback for myoelectric control.

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