Designing a Virtual Reality Myoelectric Prosthesis Training System for Amputees

Abstract

Electrical signals produced by muscle contractions are found to be effective in controlling accurately artificial limbs. Myoelectric-powered can be more functional and advantageous compared to passive or body-powered prostheses, however extensive training is required to take full advantage of the myoelectric prosthesis' usability. In recent years, computer technology has brought new opportunities for improving patients' training, resulting in more usable and functional solutions.Virtual Reality (VR) is a representative example of this type of technology. These preliminary findings suggested that myoelectric-powered training enhanced with VR can simulate a pain-free, natural, enjoyable, and realistic experience for the patient. It was also suggested that VR can complement prosthesis training, by improving the functionality of the missing body part. Finally, it was shown that VR can resolve one of the most common challenges for a new prosthesis user, which is to accept the fitting of the prosthetic device to their own body.

A Comparison of Myoelectric Control Modes for an Assistive Robotic Virtual Platform

“…Future testing will evaluate how training reduces time and improves accuracy in grasping. In previous studies, participants have performed similar myoelectric training [25], but in those cases the user only had to interact with the environment according to a single mode. In this study, the users can try different ways to interact with the assistive robot, so that they have the opportunity to choose the one that best suits their needs.…”

Current status and clinical perspectives of extended reality for myoelectric prostheses: review

“…The virtual hand is usually represented by a game element or visualisation. Interacting with objects in the XR environment typically involves attaching them to the hand through programming, rather than controlling the virtual hand using a myoelectric controller that mimics the functionality of the prosthetic hand ( Hargrove et al, 2018 ; Nissler et al, 2019 ; Phelan et al, 2021 ) compared the virtual TAC test with a set of outcome measures for physical prostheses, including SHAP, JTHF, BBT, and CRT. Their findings showed a correlation between virtual test measurement and physical performance, but no causal relationship was found.…”

Using Eye Gaze to Train an Adaptive Myoelectric Interface

“…Noninvasive myoelectric sensing has been explored in human-device interaction studies, including teleoperation [20], user understanding [43], VR/AR training [55], and upper-limb [18] and lower-limb prostheses [21], and rehabilitation [33, 47, 50, 56]. From an HCI perspective, these studies demonstrated an accessible alternative to traditional manual devices like mice and keyboards for users with upper-limb motor impairments [70].…”