Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation
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. I...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/9/12/2140 |
| _version_ | 1797544781807616000 |
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| author | Hyo Ryun Lee Jihun Park Young-Joo Suh |
| author_facet | Hyo Ryun Lee Jihun Park Young-Joo Suh |
| author_sort | Hyo Ryun Lee |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-10T14:06:18Z |
| format | Article |
| id | doaj.art-d680032d7d5242f7a283f75133bb5395 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T14:06:18Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-d680032d7d5242f7a283f75133bb53952023-11-21T00:42:49ZengMDPI AGElectronics2079-92922020-12-01912214010.3390/electronics9122140Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain AdaptationHyo Ryun Lee0Jihun Park1Young-Joo Suh2Department of Computer Science and Engineering (CSE), Pohang University of Science and Technology (POSTECH), Pohang 37673, KoreaGraduate School of Artificial Intelligence (GSAI), Pohang University of Science and Technology (POSTECH), Pohang 37673, KoreaGraduate School of Artificial Intelligence (GSAI), Pohang University of Science and Technology (POSTECH), Pohang 37673, KoreaWith 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%.https://www.mdpi.com/2079-9292/9/12/214060 GHz FMCW radardeep learningdomain adaptationhand gesture recognitionhuman activity recognition (HAR) |
| spellingShingle | Hyo Ryun Lee Jihun Park Young-Joo Suh Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation Electronics 60 GHz FMCW radar deep learning domain adaptation hand gesture recognition human activity recognition (HAR) |
| title | Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation |
| title_full | Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation |
| title_fullStr | Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation |
| title_full_unstemmed | Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation |
| title_short | Improving Classification Accuracy of Hand Gesture Recognition Based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation |
| title_sort | improving classification accuracy of hand gesture recognition based on 60 ghz fmcw radar with deep learning domain adaptation |
| topic | 60 GHz FMCW radar deep learning domain adaptation hand gesture recognition human activity recognition (HAR) |
| url | https://www.mdpi.com/2079-9292/9/12/2140 |
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