期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 70, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2021.3077967
关键词
Convolutional neural networks (CNNs); data glove; deep learning; gesture recognition; temporal convolution
资金
- National Natural Science Foundation of China [61702157]
A low-cost and efficient data glove and a novel dynamic hand gesture recognition algorithm were proposed in this study, utilizing a fusion model of convolutional neural network and generic temporal convolutional network. Extensive experiments demonstrated that the proposed algorithm outperforms many state-of-the-art algorithms on various measures.
Gesture recognition as a natural, convenient and recognizable way has been received more and more attention on human-machine interaction (HMI) recently. However, visual-based gesture recognition methods are often restricted by environments and classical wearable device-based strategies are suffered from relatively low accuracy or the complicated structures. In this study, we first design a low-cost and efficient data glove with simple hardware structure to capture finger movement and bending simultaneously. Second, a novel dynamic hand gesture recognition algorithm (DGDL-GR) is proposed to recognize human dynamic sign language, in which a fusion model of convolutional neural network (fCNN) and generic temporal convolutional network (TCN) is fully utilized. The fCNN (fusion of 1-D CNN and 2-D CNN) is proposed to extract time-domain features of finger resistance movement and spatial domain features of finger resistance bending simultaneously. Moreover, due to the superiorities of TCN in sequence modeling task, this work proposes a novel hand gesture recognition method based on the TCN, which includes causal convolution, dilation convolution, and a residual network with appropriate layers. Both long- and short-time dependencies of the hand gesture features are deeply mined and classified in the end. Results of extensive experiments have demonstrated that the proposed DGDL-GR algorithm outperforms many state-of-the-art algorithms on the measure of accuracy, F1 score, precision score, and recall score with the real-world dataset. Moreover, the number of residual blocks and some key hyperparameters of the proposed DGDL-GR algorithm has been studied thoroughly in this work.
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