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
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.13 No.4
- 발행연도
- 2018.7
- 수록면
- 1,732 - 1,739 (8page)
이용수
초록· 키워드
오류제보하기
With the explosion of digital devices, interaction technologies between human and devices are required more than ever. Especially, hand gesture recognition is advantageous in that it can be easily used. It is divided into the two groups: the contact sensor and the non-contact sensor. Compared with non-contact gesture recognition, the advantage of contact gesture recognition is that it is able to classify gestures that disappear from the sensor"s sight. Also, since there is direct contacted with the user, relatively accurate information can be acquired. Electromyography (EMG) and forcesensitive resistors (FSRs) are the typical methods used for contact gesture recognition based on muscle activities. The sensors, however, are generally too sensitive to environmental disturbances such as electrical noises, electromagnetic signals and so on. In this paper, we propose a novel contact gesture recognition method based on Flexible Epidermal Tactile Sensor Array (FETSA) that is used to measure electrical signals according to movements of the wrist. To recognize gestures using FETSA, we extracted feature sets, and the gestures were subsequently classified using the support vector machine. The performance of the proposed gesture recognition method is very promising in comparison with two previous non-contact and contact gesture recognition studies.
목차
Abstract
1. Introduction
2. Flexible Epidermal Tactile Sensor Array
3. Gesture Recognition
4. Experiments
5. Conclusion
References
참고문헌 (21)
참고문헌 신청
Chao Xu, Parth, H. Pathak and Prasant Mohapatra, “Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch”. Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, Santa Fe, NM, USA, Feb. 2015.
Zhiyuan Lu, Xiang Chen, Qiang Li, Xu Zhang, and Ping Zhou, “A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices” IEEE Transactions on Human-Machine Systems, vol. 44, no. 2, pp. 293-299, April 2014.
Xiang Chen, Xu Zhang, Zhang-Yan Zhao, Ji-Hai Yang, Vuokko Lantz and Kong-Qiao Wang, “Hand Gesture Recognition Research Based on Surface EMG Sensors and 2D-accelerometers,” Wearable Computers, 2007 11th IEEE International Symposium on, Boston, MA, USA, Oct. 2007.
Jess McIntosh, Charlie McNeill, Mike Fraser, Frederic Kerber, Markus Lochtefeld and Antonio Kruger, “EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing,” CHI '16 Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose, CA, USA, May 2007.
Yang Zhang, Chris Harrison, “Tomo: Wearable, Low- Cost Electrical Impedance Tomography for Hand Gesture Recognition,” UIST '15 Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology, pp. 167-173, Nov. 2015.
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