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A hand gesture recognition technique for human–computer interaction |
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| Affiliation: | 1. School of Biomedical Engineering, Third Military Medical University, Chongqing 400038, China;2. College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China;1. Department of Information Engineering and Computer Science, Feng Chia University, No. 100, Wenhwa Rd., Seatwen, Taichung 40724, Taiwan;2. Department of Communication Engineering, Feng Chia University, No. 100, Wenhwa Rd., Seatwen, Taichung 40724, Taiwan;3. Department of Management and International Business, University of Auckland, Auckland, New Zealand;4. College of Information and Technology, The University of Da Nang, Luu Quang Vu Rd., Da Nang City, Viet Nam;1. Department of IT Engineering, Sookmyung Women’s University, Seoul, Republic of Korea;2. Department of Computer Engineering, SunMoon University, A-san, Republic of Korea;3. School of Electrical Sciences, Indian Institute of Technology Bhubaneswar, India;1. Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, 4023 Sousse, Tunisia;2. Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, 4023 Sousse, Tunisia;3. Université de Sfax, Institut supérieure de Biotechnologie de Sfax, SETIT-Research Unit of Sciences of Electronic, Technologies of Information and Telecommunication, 3038, Tunisia |
| Abstract: | We propose an approach to recognize trajectory-based dynamic hand gestures in real time for human–computer interaction (HCI). We also introduce a fast learning mechanism that does not require extensive training data to teach gestures to the system. We use a six-degrees-of-freedom position tracker to collect trajectory data and represent gestures as an ordered sequence of directional movements in 2D. In the learning phase, sample gesture data is filtered and processed to create gesture recognizers, which are basically finite-state machine sequence recognizers. We achieve online gesture recognition by these recognizers without needing to specify gesture start and end positions. The results of the conducted user study show that the proposed method is very promising in terms of gesture detection and recognition performance (73% accuracy) in a stream of motion. Additionally, the assessment of the user attitude survey denotes that the gestural interface is very useful and satisfactory. One of the novel parts of the proposed approach is that it gives users the freedom to create gesture commands according to their preferences for selected tasks. Thus, the presented gesture recognition approach makes the HCI process more intuitive and user specific. |
| Keywords: | Dynamic gesture recognition Hand gesture Finite state machine-based recognition Gestural interfaces Gesture-based interaction Human-computer interaction Intuitive interfaces Hand trajectory recognition Adaptive gestures |
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