期刊
IEEE SENSORS JOURNAL
卷 19, 期 19, 页码 8441-8451出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2018.2877978
关键词
Assistive robots; dynamic time warping; hand trajectory gesture recognition; human-robot interaction; Lazy Snapping; smart home; SURF; wearable sensor; wrist camera
资金
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China [U1509204]
- Science Fund for Creative Research Groups of the National Natural Science Foundation of China [51521064]
- China's Thousand Talents Plan Young Professionals Program
As a promising component for body sensor networks, the wearable sensors for hand gesture recognition have increasingly received great attention in recent years. By interpreting human intentions through hand gestures, the natural human-robot interaction can be realized in the smart home where the youth and the elderly can perform hand gestures to control the household robot or the robotic wheelchair. Here, a wearable wrist-worn camera sensor was shown to recognize hand trajectory gestures. The moving velocity of the user's hand was deduced from the matched speeded up robust features keypoints of the moving background of the video sequence. Furthermore, the segmentation of continuous gestures was achieved by detecting the predefined gesture starting signal from the hand region of the image, which was segmented by the lazy snapping algorithm. In this paper, 10 types of gestures and 1350 gesture samples collected from 15 subjects at three different scenes were classified by the dynamic time warping algorithm and the results achieved an average recognition accuracy up to 97.6%. Moreover, the practicability of the proposed system was further demonstrated by controlling a cooperative robot to draw letters on paper.
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