인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
이용수
초록· 키워드
Objective: We introduce a hand gesture segmentation method using a wrist-worn wearable device which can recognize simple gestures of clenching and unclenching ones" fist.
Background: There are many types of smart watches and fitness bands in the markets. And most of them already adopt a gesture interaction to provide ease of use. However, there are many cases in which the malfunction is difficult to distinguish between the user"s gesture commands and user"s daily life motion. It is needed to develop a simple and clear gesture segmentation method to improve the gesture interaction performance.
Method: At first, we defined the gestures of making a fist (start of gesture command) and opening one"s fist (end of gesture command) as segmentation gestures to distinguish a gesture. The gestures of clenching and unclenching one"s fist are simple and intuitive. And we also designed a single gesture consisting of a set of making a fist, a command gesture, and opening one"s fist in order. To detect segmentation gestures at the bottom of the wrist, we used a wrist strap on which an array of infrared sensors (emitters and receivers) were mounted. When a user takes gestures of making a fist and opening one"s a fist, this changes the shape of the bottom of the wrist, and simultaneously changes the reflected amount of the infrared light detected by the receiver sensor.
Results: An experiment was conducted in order to evaluate gesture segmentation performance. 12 participants took part in the experiment: 10 males, and 2 females with an average age of 38. The recognition rates of the segmentation gestures, clenching and unclenching one"s fist, are 99.58% and 100%, respectively.
Conclusion: Through the experiment, we have evaluated gesture segmentation performance and its usability. The experimental results show a potential for our suggested segmentation method in the future.
Application: This can be adopted to user interface for fashion apparel such as a smart watch and wrist band.
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지Background: There are many types of smart watches and fitness bands in the markets. And most of them already adopt a gesture interaction to provide ease of use. However, there are many cases in which the malfunction is difficult to distinguish between the user"s gesture commands and user"s daily life motion. It is needed to develop a simple and clear gesture segmentation method to improve the gesture interaction performance.
Method: At first, we defined the gestures of making a fist (start of gesture command) and opening one"s fist (end of gesture command) as segmentation gestures to distinguish a gesture. The gestures of clenching and unclenching one"s fist are simple and intuitive. And we also designed a single gesture consisting of a set of making a fist, a command gesture, and opening one"s fist in order. To detect segmentation gestures at the bottom of the wrist, we used a wrist strap on which an array of infrared sensors (emitters and receivers) were mounted. When a user takes gestures of making a fist and opening one"s a fist, this changes the shape of the bottom of the wrist, and simultaneously changes the reflected amount of the infrared light detected by the receiver sensor.
Results: An experiment was conducted in order to evaluate gesture segmentation performance. 12 participants took part in the experiment: 10 males, and 2 females with an average age of 38. The recognition rates of the segmentation gestures, clenching and unclenching one"s fist, are 99.58% and 100%, respectively.
Conclusion: Through the experiment, we have evaluated gesture segmentation performance and its usability. The experimental results show a potential for our suggested segmentation method in the future.
Application: This can be adopted to user interface for fashion apparel such as a smart watch and wrist band.
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목차
- 1. Introduction
- 2. Gesture Segmentation
- 3. Prototype of Wrist-Worn Wearable Device
- 4. Algorithm
- 5. Method
- 6. Results
- 7. Discussion
- 8. Conclusion
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2016-530-002088502
