| Paper ID | BIO-11.7 |
| Paper Title |
Few-Shot Learning for Decoding Surface Electromyography for Hand Gesture Recognition |
| Authors |
Elahe Rahimian, Soheil Zabihi, Concordia University, Canada; Amir Asif, York University, Canada; Seyed Farokh Atashzar, New York University, United States; Arash Mohammadi, Concordia University, Canada |
| Session | BIO-11: Deep Learning for Physiological Signals |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Biomedical Imaging and Signal Processing: [BIO] Biomedical signal processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
This work is motivated by the recent advancements of Deep Neural Networks (DNNs) for myoelectric prosthesis control. In this regard, hand gesture recognition via surface Electromyogram (sEMG) signals has shown a high potential for improving the performance of myoelectric control prostheses. Although the recent researches in hand gesture recognition with DNNs have achieved promising results, they are still in their infancy. The recent literature uses traditional supervised learning methods that usually have poor performance if a small amount of data is available or requires adaptation to a changing task. Therefore, in this work, we develop a novel hand gesture recognition framework based on the formulation of Few-Shot Learning (FSL) to infer the required output given only one or a few numbers of training examples. Thus in this paper, we proposed a new architecture (named as FHGR which refers to “Few-shot Hand Gesture Recognition”) that learns the mapping using a small number of data and quickly adapts to a new user/gesture by combing its prior experience. The proposed approach led to 83.99% classification accuracy on new repetitions with few-shot observations, 76.39% accuracy on new subjects with few-shot observations, and 72.19% accuracy on new gestures with few-shot observations. |
