Name
Abstract | ||
|---|---|---|
Gesture recognition using sparse multichannel Surface Electromyography (sEMG) is a challenging problem, and the solutions are far from optimal from the point of view of Muscle-Computer Interface (MCI). In this work, we address this problem from the context of multi-view deep learning. A novel multi-view Convolutional Neural Network (CNN) framework is proposed by combining classical sEMG feature sets with a CNN-based deep learning model. The framework consists of two parts. In the first part, multi-view representations of sEMG are modeled in parallel by a multistream CNN, and a performance-based view construction strategy is proposed to choose the most discriminative views from classical feature sets for sEMG-based gesture recognition. In the second part, the learned multi-view deep features are fused through a view aggregation network composed of early and late fusion subnetworks, taking advantage of both early and late fusion of learned multi-view deep features. Evaluations on 11 sparse multichannel sEMG databases as well as 5 databases with both sEMG and Inertial Measurement Unit(IMU) data demonstrate that our multi-view framework outperforms singleview methods on both unimodal and multimodal sEMG data streams. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TBME.2019.2899222 | IEEE transactions on bio-medical engineering |
Keywords | Field | DocType |
Gesture recognition,Deep learning,Feature extraction,Databases,Electromyography,Human computer interaction,Discrete wavelet transforms | Computer vision,Data stream mining,Pattern recognition,Computer science,Convolutional neural network,Gesture recognition,Feature extraction,Artificial intelligence,Inertial measurement unit,Deep learning,Discriminative model | Journal |
Volume | Issue | ISSN |
66 | 10 | 1558-2531 |
Citations | PageRank | References |
5 | 0.43 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wentao Wei | 1 | 34 | 2.36 |
| Qingfeng Dai | 2 | 5 | 0.43 |
| Yongkang Wong | 3 | 377 | 29.30 |
| Yu Hu | 4 | 537 | 76.69 |
| Mohan Kankanhalli | 5 | 3825 | 299.56 |
| weidong | 6 | 7 | 1.51 |