Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition

Abstract

Wrist electromyography (EMG) signals have been explored for incorporation into subtle wrist-worn wearable devices for decoding hand gestures. Previous studies have now shown that wrist EMG can even outperform the more commonly used forearm EMG, depending on the application. However, the performance and robustness of wrist EMG-based pattern recognition systems in the presence of confounding factors remain relatively unexplored. In this paper, we investigate the day-to-day stability of wrist EMG signals to ascertain their reliability across days. The test-retest reliability of concurrently collected wrist EMG and forearm EMG signals elicited during a variety of finger and wrist gestures was evaluated over a series of days. Several classification approaches, including a novel Maximum independence domain adaptation (MIDA), were investigated to explore and mitigate the effects of natural EMG variations across days. Results showed that wrist EMG signals were reliable and relatively resilient to the negative effects of EMG variations across days. Specifically, wrist EMG-based classifiers consistently outperformed forearm EMG-based classifiers with statistically significant differences ( $p < 0.05$ ) and had average classification accuracies between 93.8% - 95.7% compared to 91.3% - 92.6% for the forearm EMG-based classifiers using a novel Inter-Day Feature Set (IDFS) and a novel adaptive-MIDA linear discriminant analysis (LDA) classification technique requiring minimal training. This study builds further evidence for the viability of commercial wrist-worn EMG wearables with minimal training burden for general consumers.

Keywords

• Adaptive classification
• domain adaptation
• gesture recognition
• stability
• wearables
• wrist EMG