Virtual Integration Environment as an Advanced Prosthetic Limb Training Platform

Abstract

Background: Despite advances in prosthetic development and neurorehabilitation, individuals with upper extremity (UE) loss continue to face functional and psychosocial challenges following amputation. Recent advanced myoelectric prostheses offer intuitive control over multiple, simultaneous degrees of motion and promise sensory feedback integration, but require complex training to effectively manipulate. We explored whether a virtual reality simulator could be used to teach dexterous prosthetic control paradigms to individuals with UE loss.Methods: Thirteen active-duty military personnel with UE loss (14 limbs) completed twenty, 30-min passive motor training sessions over 1–2 months. Participants were asked to follow the motions of a virtual avatar using residual and phantom limbs, and electrical activity from the residual limb was recorded using surface electromyography. Eight participants (nine limbs), also completed twenty, 30-min active motor training sessions. Participants controlled a virtual avatar through three motion sets of increasing complexity (Basic, Advanced, and Digit) and were scored on how accurately they performed requested motions. Score trajectory was assessed as a function of time using longitudinal mixed effects linear regression.Results: Mean classification accuracy for passive motor training was 43.8 ± 10.7% (14 limbs, 277 passive sessions). In active motor sessions, >95% classification accuracy (which we used as the threshold for prosthetic acceptance) was achieved by all participants for Basic sets and by 50% of participants in Advanced and Digit sets. Significant improvement in active motor scores over time was observed in Basic and Advanced sets (per additional session: β-coefficient 0.125, p = 0.022; β-coefficient 0.45, p = 0.001, respectively), and trended toward significance for Digit sets (β-coefficient 0.594, p = 0.077).Conclusions: These results offer robust evidence that a virtual reality training platform can be used to quickly and efficiently train individuals with UE loss to operate advanced prosthetic control paradigms. Participants can be trained to generate muscle contraction patterns in residual limbs that are interpreted with high accuracy by computer software as distinct active motion commands. These results support the potential viability of advanced myoelectric prostheses relying on pattern recognition feedback or similar controls systems.

Clinical evaluation of the revolutionizing prosthetics modular prosthetic limb system for upper extremity amputees

“…The VIE allows individuals to direct the movements of a virtual avatar using surface electromyogram (sEMG) signals captured from their residual limbs, digitized through an electrically-isolated data acquisition system, and filtered using signal analysis algorithms. Our laboratory has previously described the utility of the VIE in training individuals with upper extremity amputation in this pattern recognition platform for prosthetic control 21 , 22 . Motions included hand open, wrist flexion, extension, pronation, and supination, several user-selected grasps, “no movement”, and elbow flexion and extension with trans-humeral amputation.…”

“…Signal feature extraction and motion classification occurred using machine learning-based pattern recognition software and a Linear Discriminant Analysis classifier. The same software interfaces are used to control the MPL prosthesis 17 , 21 , 22 .…”

Comparison of Dexterous Task Performance in Virtual Reality and Real-World Environments

“…While these activities can be completed with a physical prosthetic device, training in a virtual environment has shown to be an effective way to train amputees to use their device (Phelan et al, 2015;Nakamura et al, 2017;Perry et al, 2018;Nissler et al, 2019). Training in a virtual environment can be a cost effective way for clinics to perform rehabilitation (Phelan et al, 2015;Nakamura et al, 2017) and help prosthesis users learn how to manipulate their device using its particular control scheme (Blana et al, 2016;Woodward and Hargrove, 2018), and gamifying rehabilitation has been shown to increase a prosthesis user's desire to complete the program (Prahm et al, 2017(Prahm et al, , 2018.…”

Technological Advances in Prosthesis Design and Rehabilitation Following Upper Extremity Limb Loss

“…Putrino et al [76] trained two nonhuman primates (Macaca mulatta) to use the virtual prosthetic and observed that they performed reaching and grasping tasks. The authors describe virtual prostheses as highly versatile and Perry et al [77] have demonstrated that a virtual reality (VR) training platform can be used to efficiently train upper extremity amputees. In this study, thirteen active-duty military personnel with 14 upper extremity amputations learned to control a virtual avatar over 1 to 2 months.…”