Abstract
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.
Highlights
The ease with which adults use their hands is owed to an intricate feedforward-feedback mechanism that has been honed since birth (Johansson and Cole, 1992)
Feedforward control of myoelectric hand prostheses is influenced by two factors: (1) the robustness of the control of the movements of the prosthesis, which is affected by the method of recording and decoding the users’ intent (Geethanjali, 2016). (2) the users’ ability to produce these control signals that is dependent on their understanding of the system – how it is represented in the central nervous system – which is known as the internal model (Kawato, 1999)
Our findings show that all augmented feedback types significantly improved the performance compared to vision alone in the functional task, but only the audio biofeedback (VA) had an effect on the internal model strength, as measured by the psychophysical framework/metrics
Summary
The ease with which adults use their hands is owed to an intricate feedforward-feedback mechanism that has been honed since birth (Johansson and Cole, 1992). Prosthesis users rely, on proprioception in the remaining muscles (sense of contraction), visual feedback and, to some extent, on the incidental feedback that motor noise, and socket vibration provide (Simpson, 1973; Childress, 1980; Antfolk et al, 2013; Markovic et al, 2018b). They cannot adequately hone their internal model, which negatively affects their ability to control the prosthesis (Lum et al, 2014; Shehata et al, 2018c). It is a desirable goal to restore natural closedloop control with supplementary (explicit) sensory feedback
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