Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition
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...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9966602/ |
| _version_ | 1811178467346087936 |
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| author | Fady S. Botros Angkoon Phinyomark Erik J. Scheme |
| author_facet | Fady S. Botros Angkoon Phinyomark Erik J. Scheme |
| author_sort | Fady S. Botros |
| collection | DOAJ |
| description | 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 (<inline-formula> <tex-math notation="LaTeX">$p < 0.05$ </tex-math></inline-formula>) 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. |
| first_indexed | 2024-04-11T06:18:48Z |
| format | Article |
| id | doaj.art-a22fb6af7beb4dd3a8dc4dea0cb3a2ec |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T06:18:48Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a22fb6af7beb4dd3a8dc4dea0cb3a2ec2022-12-22T04:40:52ZengIEEEIEEE Access2169-35362022-01-011012594212595410.1109/ACCESS.2022.32257619966602Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture RecognitionFady S. Botros0https://orcid.org/0000-0002-9311-1345Angkoon Phinyomark1https://orcid.org/0000-0003-0170-3245Erik J. Scheme2https://orcid.org/0000-0002-4421-1016Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, CanadaInstitute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, CanadaDepartment of Electrical and Computer Engineering, University of New Brunswick, Fredericton, CanadaWrist 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 (<inline-formula> <tex-math notation="LaTeX">$p < 0.05$ </tex-math></inline-formula>) 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.https://ieeexplore.ieee.org/document/9966602/Adaptive classificationdomain adaptationgesture recognitionstabilitywearableswrist EMG |
| spellingShingle | Fady S. Botros Angkoon Phinyomark Erik J. Scheme Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition IEEE Access Adaptive classification domain adaptation gesture recognition stability wearables wrist EMG |
| title | Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition |
| title_full | Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition |
| title_fullStr | Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition |
| title_full_unstemmed | Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition |
| title_short | Day-to-Day Stability of Wrist EMG for Wearable-Based Hand Gesture Recognition |
| title_sort | day to day stability of wrist emg for wearable based hand gesture recognition |
| topic | Adaptive classification domain adaptation gesture recognition stability wearables wrist EMG |
| url | https://ieeexplore.ieee.org/document/9966602/ |
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