Abstract

Upper limb prostheses are specialized tools, and skilled operation is learned by amputees over time. Recently, neural prostheses using implanted peripheral nerve interfaces have enabled advances in artificial somatosensory feedback that can improve prosthesis outcomes. However, the effect of sensory learning on artificial somatosensation has not been studied, despite its known influence on intact somatosensation and analogous neuroprostheses. Sensory learning involves changes in the perception and interpretation of sensory feedback and may further influence functional and psychosocial outcomes. In this mixed methods case study, we examined how passive learning over 115 days of home use of a neural-connected, sensory-enabled prosthetic hand influenced perception of artificial sensory feedback in a participant with transradial amputation. We examined perceptual changes both within individual days of use and across the duration of the study. At both time scales, the reported percept locations became significantly more aligned with prosthesis sensor locations, and the phantom limb became significantly more extended toward the prosthesis position. Similarly, the participant’s ratings of intensity, naturalness, and contact touch significantly increased, while his ratings of vibration and movement significantly decreased across-days for tactile channels. These sensory changes likely resulted from engagement of cortical plasticity mechanisms as the participant learned to use the artificial sensory feedback. We also assessed psychosocial and functional outcomes through surveys and interviews, and found that self-efficacy, perceived function, prosthesis embodiment, social touch, body image, and prosthesis efficiency improved significantly. These outcomes typically improved within the first month of home use, demonstrating rapid benefits of artificial sensation. Participant interviews indicated that the naturalness of the experience and engagement with the prosthesis increased throughout the study, suggesting that artificial somatosensation may decrease prosthesis abandonment. Our data showed that prosthesis embodiment was intricately related to naturalness and phantom limb perception, and that learning the artificial sensation may have modified the body schema. As another indicator of successfully learning to use artificial sensation, the participant reported the emergence of stereognosis later in the study. This study provides the first evidence that artificial somatosensation can undergo similar learning processes as intact sensation and highlights the importance of sensory restoration in prostheses.

Highlights

  • IntroductionProstheses for upper limb amputees are considered to be a special case of tool use, because their purpose is to replace a missing body part rather than to augment normal human capabilities

  • Tool use is a ubiquitous human trait

  • In our prior home use study, we found that sensory feedback impacted the psychosocial experience of prosthesis embodiment, confidence, and perceived efficiency

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Summary

Introduction

Prostheses for upper limb amputees are considered to be a special case of tool use, because their purpose is to replace a missing body part rather than to augment normal human capabilities. Somatosensation has been restored in upper limb prostheses, either through noninvasive electrocutaneous and vibrotactile techniques (Dietrich et al, 2012, 2018; Clemente et al, 2016) or through implanted neural interfaces (Raspopovic et al, 2014; Tan et al, 2014; Davis et al, 2016). Multiple groups have investigated direct electrical stimulation of the remaining nerves as a means of restoring sensation of the missing hand to upper limb amputees (Raspopovic et al, 2014; Tan et al, 2014; Davis et al, 2016). In addition to quantifying the evoked percepts (Tan et al, 2014; Graczyk et al, 2016, 2018a), these groups have shown marked improvements in performance of functional tasks, such as object identification (Schiefer et al, 2016), object feature discrimination (Horch et al, 2011; Raspopovic et al, 2014; Oddo et al, 2016; Schiefer et al, 2018), and closed-loop control (Wendelken et al, 2017; Valle et al, 2018), when sensory feedback is provided

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