Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks
2019-01-21Code Available1· sign in to hype
István Ketykó, Ferenc Kovács, Krisztián Zsolt Varga
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ReproduceCode
- github.com/ketyi/2SRNNOfficialIn papertf★ 39
Abstract
Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and HighDensity (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.
Tasks
Benchmark Results
| Dataset | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| CapgMyo DB-a | 2SRNN | Accuracy | 97.1 | — | Unverified |
| CapgMyo DB-b | 2SRNN | Accuracy | 97.1 | — | Unverified |
| CapgMyo DB-c | 2SRNN | Accuracy | 96.8 | — | Unverified |
| Ninapro DB-1 12 gestures | 2SRNN | Accuracy | 84.7 | — | Unverified |
| Ninapro DB-1 8 gestures | 2SRNN | Accuracy | 90.7 | — | Unverified |