Frontiers in Bioengineering and Biotechnology (Oct 2024)
Physio-avatar EB: aftereffects in error learning with EMG manipulation of first-person avatar experience
Abstract
IntroductionMany studies have investigated the manipulation of a virtual upper arm using electromyogram (EMG); however, these studies primarily used a machine learning model or trigger control for this purpose. Furthermore, most of them could only display the constant motion of the virtual arm because the motion to be displayed was selected by pattern recognition or trigger control. In addition, these studies did not examine changes in the electromyographic signals after experiencing the virtual arm. By contrast, we propose a real-time, continuous, learning-free avatar that manipulates the virtual arm with electromyogram signals or physio-avatar EMG biofeedback (EB). The goal of the physio-avatar EB system is to induce physiological changes through experiential interactions.MethodsWe explored the possibility of changing motor control strategies by applying the system to healthy individuals as a case study. An intervention method that provided an experience of a body different from one’s own was conducted on seven participants using a time-invariant calculation algorithm to determine the joint angles of the avatar. Control strategies for an indicator of the equilibrium point in the baseline and adaptation phases were determined to evaluate the physio-avatar EB intervention effect. The similarity of these BL and adaptation control strategies compared to those used during the washout period was assessed using the coefficient of determination. The accuracy and reliability of the virtual reality (VR) system were evaluated by comparison with existing studies and the required specs.Results and DiscussionChanges in motor control strategies due to the physio-avatar EB system were observed in four experiments, where the participants gradually returned to their pre-intervention control strategies. This result can be attributed to the aftereffects caused by error learning. This implies that the developed system influenced their motor control strategies. The number of EMG acquisition bits was 16 bits, and the sampling rate was 1,000 Hz. The refresh rate of the head-mounted display was 90 Hz, and its resolution was 1440×1600 for a single eye. Additionally, the simulation frame rate was 30 FPS. These values were adequate compared to existing studies and required specs. The essential contribution of this study is the development of an avatar that is controlled by a different method than has been used in previous studies and the demonstration of changes in a subject’s muscle activity after they experience an avatar. In the future, the clinical efficacy of the proposed system will be evaluated with actual patients.
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