Audible Feedback Improves Internal Model Strength and Performance of Myoelectric Prosthesis Control

Ahmed W. Shehata,Erik J. Scheme,Jonathon W. Sensinger

doi:10.1038/s41598-018-26810-w

Ahmed W. Shehata, Erik J. Scheme + Show 1 more

Open Access

https://doi.org/10.1038/s41598-018-26810-w

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Abstract

Myoelectric prosthetic devices are commonly used to help upper limb amputees perform activities of daily living, however amputees still lack the sensory feedback required to facilitate reliable and precise control. Augmented feedback may play an important role in affecting both short-term performance, through real-time regulation, and long-term performance, through the development of stronger internal models. In this work, we investigate the potential tradeoff between controllers that enable better short-term performance and those that provide sufficient feedback to develop a strong internal model. We hypothesize that augmented feedback may be used to mitigate this tradeoff, ultimately improving both short and long-term control. We used psychometric measures to assess the internal model developed while using a filtered myoelectric controller with augmented audio feedback, imitating classification-based control but with augmented regression-based feedback. In addition, we evaluated the short-term performance using a multi degree-of-freedom constrained-time target acquisition task. Results obtained from 24 able-bodied subjects show that an augmented feedback control strategy using audio cues enables the development of a stronger internal model than the filtered control with filtered feedback, and significantly better path efficiency than both raw and filtered control strategies. These results suggest that the use of augmented feedback control strategies may improve both short-term and long-term performance.

Highlights

The role of feedback for real-time regulation and in improving human understanding of the control system and the task being performed is still unclear
We hypothesize that audio augmented feedback may improve the performance of myoelectric prosthesis control by enabling the development of stronger internal models
To inform the development of myoelectric control strategies with better short- and long-term performance, we investigated whether augmented feedback could improve user’s internal model strength without reducing short-term performance of the control

Summary

Introduction

The role of feedback for real-time regulation and in improving human understanding of the control system and the task being performed is still unclear. We found that control strategies with filtered control signals and reduced feedback (Filtered Control with Filtered Feedback (FLT), such as classification-based control) may enable better short-term performance, but hinder the development of internal models. To mitigate this tradeoff, we have extended this work by decoupling the concepts of control and feedback through the use of augmented feedback. We combined the filtered control strategy that resulted in better short-term performance with audio augmented feedback from the raw control strategy that enables the development of stronger internal models. Our results show that the audio augmented feedback control strategy produces better short-term performance (as assessed using path efficiency and accuracy) than the feedback-rich control strategy, while enabling the development of a stronger internal model than the reduced feedback control strategy

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