KOA-CLSTM-based real-time dynamic hand gesture recognition on mobile terminal

Hou, Xianghui; Cen, Sijia; Zhang, Meiyu; Jian, Chengfeng

doi:10.1007/s11760-022-02395-w

Cited by 1 publication

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“…The software used in the experiment and the interaction between the software are described in Section 4.2. Additionally, a demo video about the experiment setting for the data collection and real-time experiment can referred to in [51].…”

Section: Methodsmentioning

confidence: 99%

Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation

2020

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With the recent development of small radars with high resolution, various human–computer interaction (HCI) applications using them have been developed. In particular, a method of applying a user’s hand gesture recognition using a short-range radar to an electronic device is being actively studied. In general, the time delay and Doppler shift characteristics that occur when a transmitted signal that is reflected off an object returns are classified through deep learning to recognize the motion. However, the main obstacle in the commercialization of radar-based hand gesture recognition is that even for the same type of hand gesture, recognition accuracy is degraded due to a slight difference in movement for each individual user. To solve this problem, in this paper, the domain adaptation is applied to hand gesture recognition to minimize the differences among users’ gesture information in the learning and the use stage. To verify the effectiveness of domain adaptation, a domain discriminator that cheats the classifier was applied to a deep learning network with a convolutional neural network (CNN) structure. Seven different hand gesture data were collected for 10 participants and used for learning, and the hand gestures of 10 users that were not included in the training data were input to confirm the recognition accuracy of an average of 98.8%.

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Section: Methodsmentioning

confidence: 99%