Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 윤종원 | - |
dc.date.accessioned | 2023-01-03T05:22:12Z | - |
dc.date.available | 2023-01-03T05:22:12Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.citation | International Conference on ICT Convergence, article no. 9289218, Page. 1699-1704 | - |
dc.identifier.issn | 2162-1233 | - |
dc.identifier.uri | https://ieeexplore.ieee.org/document/9289218 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/178605 | - |
dc.description.abstract | With the emergence of smart home appliances and AR/VR content, the demand for a better user interface is constantly increasing. Traditional user interface, touch-based interface is no longer applied to AR/VR applications and vision or RF-based gesture recognition requires camera and sensors, resulting in additional cost. The accuracy of above-mentioned methods highly depends on brightness and surrounding environment, therefore they fail to guarantee robustness. There are several researches on acoustic-based gesture recognition, but are limited to single point movement such as straight line, circle and triangle. In this paper, we design and implement multi-point gesture recognition system utilizing both the acoustic signals and machine learning technique. We use channel impulse response (CIR) containing multi-path information to recognize multi-point gestures, and construct a CNN model to learn its features. In addition, we present a method of constructing a CNN model suitable for gesture recognition. Evaluation results show that our system successfully recognizes multi-point gestures and demonstrate its efficacy. ? 2020 IEEE. | - |
dc.description.sponsorship | This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF2018R1C1B6006436). | - |
dc.language | en | - |
dc.publisher | IEEE Computer Society | - |
dc.subject | Acoustic signal | - |
dc.subject | CIR | - |
dc.subject | CNN | - |
dc.subject | Gesture recognition | - |
dc.title | Multi-Point Gesture Recognition Leveraging Acoustic Signals and CNN | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICTC49870.2020.9289218 | - |
dc.relation.page | 1699-1704 | - |
dc.relation.journal | International Conference on ICT Convergence | - |
dc.contributor.googleauthor | Shin, Donghwan | - |
dc.contributor.googleauthor | Yoon, Jongwon | - |
dc.sector.campus | E | - |
dc.sector.daehak | 소프트웨어융합대학 | - |
dc.sector.department | 소프트웨어학부 | - |
dc.identifier.pid | jongwon | - |
dc.identifier.article | 9289218 | - |
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