Context Informed Incremental Learning Improves Myoelectric Control Performance in Virtual Reality Object Manipulation Tasks

Abstract

Electromyography (EMG)-based gesture recognition is a promising approach for designing intuitive human-computer interfaces. However, while these systems typically perform well in controlled laboratory settings, their usability in real-world applications is compromised by declining performance during real-time control. This decline is largely due to goal-directed behaviors that are not captured in static, offline scenarios. To address this issue, we use \textit{Context Informed Incremental Learning} (CIIL) - marking its first deployment in an object-manipulation scenario - to continuously adapt the classifier using contextual cues. Nine participants without upper limb differences completed a functional task in a virtual reality (VR) environment involving transporting objects with life-like grips. We compared two scenarios: one where the classifier was adapted in real-time using contextual information, and the other using a traditional open-loop approach without adaptation. The CIIL-based approach not only enhanced task success rates and efficiency, but also reduced the perceived workload by 7.1 %, despite causing a 5.8 % reduction in offline classification accuracy. This study highlights the potential of real-time contextualized adaptation to enhance user experience and usability of EMG-based systems for practical, goal-oriented applications, crucial elements towards their long-term adoption. The source code for this study is available at: https://github.com/BiomedicalITS/ciil-emg-vr.