Multiperspective analysis reveals neurorobotic sensory and motor fusion in a bionic system promotes intrinsic neural behaviors.

On-going developments in myoelectric prosthesis control have provided prosthesis users with an assortment of control strategies that vary in reliability and performance. Many studies have focused on improving performance by providing feedback to the user but have overlooked the effect of this feedback on internal model development, which is key to improve long-term performance. In this paper, the strength of internal models developed for two commonly used myoelectric control strategies: raw control with raw feedback (using a regression-based approach) and filtered control with filtered feedback (using a classifier-based approach), were evaluated using two psychometric measures: trial-by-trial adaptation and just-noticeable difference. The performance of both strategies was also evaluated using Schmidt's style target acquisition task. Results obtained from 24 able-bodied subjects showed that although filtered control with filtered feedback had better short-term performance in path efficiency ( ), raw control with raw feedback resulted in stronger internal model development ( ), which may lead to better long-term performance. Despite inherent noise in the control signals of the regression controller, these findings suggest that rich feedback associated with regression control may be used to improve human understanding of the myoelectric control system.

BackgroundThe loss of an arm presents a substantial challenge for upper limb amputees when performing activities of daily living. Myoelectric prosthetic devices partially replace lost hand functions; however, lack of sensory feedback and strong understanding of the myoelectric control system prevent prosthesis users from interacting with their environment effectively. Although most research in augmented sensory feedback has focused on real-time regulation, sensory feedback is also essential for enabling the development and correction of internal models, which in turn are used for planning movements and reacting to control variability faster than otherwise possible in the presence of sensory delays.MethodsOur recent work has demonstrated that audio-augmented feedback can improve both performance and internal model strength for an abstract target acquisition task. Here we use this concept in controlling a robotic hand, which has inherent dynamics and variability, and apply it to a more functional grasp-and-lift task. We assessed internal model strength using psychophysical tests and used an instrumented Virtual Egg to assess performance.ResultsResults obtained from 14 able-bodied subjects show that a classifier-based controller augmented with audio feedback enabled stronger internal model (p = 0.018) and better performance (p = 0.028) than a controller without this feedback.ConclusionsWe extended our previous work and accomplished the first steps on a path towards bridging the gap between research and clinical usability of a hand prosthesis. The main goal was to assess whether the ability to decouple internal model strength and motion variability using the continuous audio-augmented feedback extended to real-world use, where the inherent mechanical variability and dynamics in the mechanisms may contribute to a more complicated interplay between internal model formation and motion variability. We concluded that benefits of using audio-augmented feedback for improving internal model strength of myoelectric controllers extend beyond a virtual target acquisition task to include control of a prosthetic hand.

State of the art myoelectric hand prostheses can restore some feedforward motor function to their users, but they cannot yet restore sensory feedback. It has been shown, using psychophysical tests, that multi-modal sensory feedback is readily used in the formation of the users’ representation of the control task in their central nervous system – their internal model. Hence, to fully describe the effect of providing feedback to prosthesis users, not only should functional outcomes be assessed, but so should the internal model. In this study, we compare the complex interactions between two different feedback types, as well as a combination of the two, on the internal model, and the functional performance of naïve participants without limb difference. We show that adding complementary audio biofeedback to visual feedback enables the development of a significantly stronger internal model for controlling a myoelectric hand compared to visual feedback alone, but adding discrete vibrotactile feedback to vision does not. Both types of feedback, however, improved the functional grasping abilities to a similar degree. Contrary to our expectations, when both types of feedback are combined, the discrete vibrotactile feedback seems to dominate the continuous audio feedback. This finding indicates that simply adding sensory information may not necessarily enhance the formation of the internal model in the short term. In fact, it could even degrade it. These results support our argument that assessment of the internal model is crucial to understanding the effects of any type of feedback, although we cannot be sure that the metrics used here describe the internal model exhaustively. Furthermore, all the feedback types tested herein have been proven to provide significant functional benefits to the participants using a myoelectrically controlled robotic hand. This article, therefore, proposes a crucial conceptual and methodological addition to the evaluation of sensory feedback for upper limb prostheses – the internal model – as well as new types of feedback that promise to significantly and considerably improve functional prosthesis control.