Electromyography-Based Gesture Recognition: Is It Time to Change Focus From the Forearm to the Wrist?

• Published in: IEEE transactions on industrial informatics Vol. 18; no. 1; pp. 174 - 184
• Main Authors: Botros, Fady S., Phinyomark, Angkoon, Scheme, Erik J.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Electrodes; Electromyography; Electromyography (EMG); Feasibility; finger gestures; Forearm; Gesture recognition; Hand (anatomy); hand gestures; human–computer interaction (HCI); Muscles; muscle–computer interface; myoelectric control; Pattern recognition; Pattern recognition systems; Prostheses; Prosthetics; Signal quality; Wearable computers; wearables; Wrist
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2020.3041618

Despite a historical focus on prosthetics, the incorporation of electromyography (EMG) sensors into less obtrusive wearable designs has recently gained attention as a potential human-computer interaction scheme for general consumer use. Because consumers are more used to wrist-worn devices, this article presents a comprehensive and systematic investigation of the feasibility of hand gesture recognition using EMG signals recorded at the wrist. A direct comparison of signal and information quality is conducted between concurrently recorded wrist and forearm signals. Both signals were collected simultaneously from 21 subjects while they performed a selection of 17 different single-finger gestures, multifinger gestures, and wrist gestures. Wrist EMG signals yielded consistently higher (<inline-formula><tex-math notation="LaTeX">p< 0.05</tex-math></inline-formula>) signal quality metrics than forearm signals for gestures that involved fine finger movements, while maintaining comparable quality for wrist gestures. Similarly, the performance of both individual state-of-the-art EMG features and a standard feature set was found to be significantly better when using wrist signals for single and multifinger gestures, and comparable for wrist gestures. Classifiers trained and tested using wrist EMG signals achieved average accuracy levels of 92.1% for single-finger gestures, 91.2% for multifinger gestures, and 94.7% for the conventional wrist gestures. In conclusion, this article clearly demonstrates the feasibility of using wrist EMG signals for hand gesture recognition. Results highlight not only the promise of this approach, but also the viability of incorporating prior knowledge from the prosthetics field in the design of wrist-based EMG pattern recognition systems.