Abstract
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%.
Highlights
In recent years, with the remarkable development of smart devices, human activity recognition (HAR) technology is being actively applied in various fields such as entertainment, healthcare, security, public safety, industry, and autonomous vehicles [1]
The frequency modulated continuous wave (FMCW) radar used for hand gesture recognition was a Hatvan module manufactured by Infineon, which is similar to the Google Soli [35,36] module
This paper proposed a 60 GHz FMCW radar-based hand gesture recognition system and a paper proposed a 60accuracy
Summary
With the remarkable development of smart devices, human activity recognition (HAR) technology is being actively applied in various fields such as entertainment, healthcare, security, public safety, industry, and autonomous vehicles [1]. In [20], user motion was classified by a deep learning model based on a random forest algorithm using Doppler images of a 60 GHz radar. Using these as input data of the LSTM-based deep learning model, the gesture recognition result was inferred. Due to the difference between the gesture information used in the learning stage and the gesture information input from the user in actual use, the accuracy of classifying results by the deep neural network is degraded. To solve this problem, this paper introduced a domain adaptation algorithm to the hand gesture recognition system.
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