지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.9
- 수록면
- 711 - 716 (6page)
- DOI
- 10.5302/J.ICROS.2023.23.0096
이용수
초록· 키워드
오류제보하기
This paper introduces a lightweight deep learning model for human-hand-gesture recognition, leveraging point cloud data acquired from a mmWave radar. The proposed 2D projection method can be applied for the preprocessing of input data for lightweight deep learning models by effectively preserving the spatial and coordinate information of each point within the 3D voxel point cloud. In addition, we proposed a 2D-CNN-TCN deep learning model that significantly reduces the number of learnable parameters while maintaining or improving the accuracy of hand-gesture recognition. The mmWave radar sensor module used in this study was IWR6843AoPEVM from Texas Instruments, and a comprehensive dataset consisting of nine distinct hand gestures was collected, with each gesture captured over a duration of 20–25 min, resulting in a total collection time of 190 min. The proposed model was trained and evaluated on a general-purpose PC. The proposed 2D-CNN-TCN model was compared to the 3D-CNN-LSTM model to reflect the 3D voxel input and time-series characteristics. The performance evaluation demonstrated that the performance of the proposed model was 1.3% enhanced with respect to the 3D-CNN-LSTM model, resulting in a recognition accuracy of 95.06% for the proposed model. Moreover, the proposed model achieved a 5.5% reduction in the number of model parameters with respect to the 3D-CNN-LSTM model. Furthermore, the lightweight deep learning model was successfully deployed as an Android application, and the usability of the model was verified through real-time hand-gesture recognition.
목차
Abstract
Ⅰ. 서론
Ⅱ. 관련 연구
Ⅲ. 데이터 수집 및 전처리
Ⅳ. 손동작 인식 경량화 모델
Ⅴ. 실험 및 검증
Ⅵ. 결론
REFERENCES
참고문헌 (10)
참고문헌 신청
Y. U. Jo and D. C. Oh, “EMG-based hand gesture recognition technique applicable according to user environment," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 11, pp. 1067-1073, 2022.
J. W. Lee, K. J. Kim, and K. H. Yu, “Implementation of a user-friendly drone control interface using hand gestures and vibrotactile feedback," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 4, pp. 349-352, 2022.
N. Zengeler, T. Kopinski, and U. Handmann, “Hand gesture recognition in automotive human machine interaction using depth cameras," Sensors, vol. 19, no. 1, Dec. 2018. doi: https://doi.org/10.3390/s19010059
A. D. Singh, S. S. Sandha, L. Garcia, and M. Srivastava, “RadHAR: Human activity recognition from point clouds generated through a millimeter-wave radar," Proc. of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems, pp. 51-56, 2019, vol.
L. Ge, Y. Cai, J. Weng, and J. Yuan, “Hand PointNet: 3D hand pose estimation using point sets," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8417-8426, 2018 doi: https://doi.org/10.1109/CVPR.2018.00878
J. W. Lee, K. J. Kim, and K. H. Yu, “Implementation of a user-friendly drone control interface using hand gestures and vibrotactile feedback," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 4, pp. 349-352, 2022.
N. Zengeler, T. Kopinski, and U. Handmann, “Hand gesture recognition in automotive human machine interaction using depth cameras," Sensors, vol. 19, no. 1, Dec. 2018. doi: https://doi.org/10.3390/s19010059
A. D. Singh, S. S. Sandha, L. Garcia, and M. Srivastava, “RadHAR: Human activity recognition from point clouds generated through a millimeter-wave radar," Proc. of the 3rd ACM Workshop on Millimeter-wave Networks and Sensing Systems, pp. 51-56, 2019, vol.
L. Ge, Y. Cai, J. Weng, and J. Yuan, “Hand PointNet: 3D hand pose estimation using point sets," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8417-8426, 2018 doi: https://doi.org/10.1109/CVPR.2018.00878
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