Adapting myoelectric control in real-time using a virtual environment

Abstract

BackgroundPattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device.MethodsHere we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance.ResultsWe found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees (P < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers.ConclusionThese results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.Electronic supplementary materialThe online version of this article (10.1186/s12984-019-0480-5) contains supplementary material, which is available to authorized users.

Understanding Limb Position and External Load Effects on Real-Time Pattern Recognition Control in Amputees

“…The 400g load, representing a lightweight prosthesis, was attached throughout training. This is an adaptation of a method suggested in previous studies [22], [23], [25], [27].…”

“…2) Real-Time Testing Protocol: In each session, subjects completed a set of 3D VR TAC tests that were based on previous work [23]. Each test comprised one trial for each movement class (except 'no movement') in each limb position.…”

“…Furthermore, we hypothesized that increasing the load would decrease controller performance, as it has been shown to affect PR sensitivity [33]. Finally, based on previous offline and real-time studies, we hypothesized that the dynamic training method will reduce the effects of limb position and load and improve controller performance [11], [22], [23], [25], [27].…”

“…Most of these experiments only evaluated offline performance, which does not always correlate with real-time controllability [30], [31]. To our knowledge, only two studies evaluated the real-time effects of limb position on PR control in amputees [23], [29]. These effects were found to be minor; however, these studies were limited because amputee subjects completed experiments without a physical prosthesis or load on the residual limb.…”

The Merits of Dynamic Data Acquisition for Realistic Myocontrol

“…Batzianoulis et al (2018) verified that dynamic training data collected during the reach-to-grasp phase of the prehension improved myocontrol stability during an online pick-and-place task. Similarly, Yang et al (2017a) and Woodward and Hargrove (2019) acquired training sEMG data while moving the arm and tested the resulting myocontrol models by engaging the participants in online tests derived from, respectively, the target achievement control and the box-and-blocks tests. Both studies confirmed that the performance of myocontrol in online settings improves when the training data is acquired while changing the arm configuration rather than keeping the arm steady in one position.…”