Adapting myoelectric control in real-time using a virtual environment
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RESEARCH
Open Access
Adapting myoelectric control in real-time using a virtual environment Richard B. Woodward1,2*
and Levi J. Hargrove1,2,3
Abstract Background: Pattern 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. Methods: Here 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. Results: We 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. Conclusion: These 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. Keywords: Amputee, Electromyography, Upper-limb prostheses, Pattern recognition, Virtual rehabilitation, Virtual guided training, Serious gaming, Real-time adaptation, Myoelectric control
Background In the US, more than 600,000 people are estimated to be living with an upper limb amputation as a result of trauma, dysvascular disease, or cancer [1]. Although prostheses have been in use for centuries [2], they still lack the functionality and dexterity of a human hand/ arm, resulting in device abandonment and diminished functional outcomes [3, 4]. Myoelectric devices—which are controlled by electromyographic (EMG) signals generated by contraction of residual muscles—provide many benefits over body-powered prostheses. Along with providing more degrees of freedom (DOFs) and the addition of net power to assist in grasping heavy items, myoelectric devices are typically more intuitive * Correspondence: [email protected] 1 Center for Bionic Medicine, Shirley Ryan Ability Lab, Chicago, IL 60611, USA 2 Department of Physical Medicine & Rehabilitation, Northwestern University, Chicago, IL 60611, USA Full list of author information is available at the end of the article
to control. The user can control the prosthesis by contracting muscles that would be used to perform desired postures (e.g., hand open, wrist flexion) in an intact
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