期刊
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
卷 66, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2021.102444
关键词
Depth vision; Hands gesture recognition; Machine learning; Clustering; Classification
资金
- European Unions' Horizon 2020 research and innovation program under SMARTsurg project [732515]
A novel autonomous learning framework is proposed in this paper to integrate the benefits of both depth vision and EMG signals, achieving real-time hand gesture recognition. Experimental results demonstrate prominent performance by introducing depth information for real-time data labeling.
Hand gesture recognition using surface electromyography (sEMG) has been one of the most efficient motion analysis techniques in human-computer interaction in the last few decades. In particular, multichannel sEMG techniques have achieved stable performance in hand gesture recognition. However, the general solution of collecting and labeling large data manually leads to time-consuming implementation. A novel learning method is therefore needed to facilitate efficient data collection and preprocessing. In this paper, a novel autonomous learning framework is proposed to integrate the benefits of both depth vision and EMG signals, which automatically label the class of collected EMG data using depth information. It then utilizes a multiple layer neural network (MNN) classifier to achieve real-time recognition of the hand gestures using only the sEMG. The overall framework is demonstrated in an augmented reality application by the recognition of 10 hand gestures using the Myo armband and an HTC VIVE PRO. The results show prominent performance by introducing depth information for real-time data labeling.
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