Browsing by Author "He, Yu-Zhen"

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COMPUTER-ASSISTED SELF-TRAINING SYSTEM FOR SPORTS EXERCISE USING KINECTS
(2013)
Chen, Hua-Tsung
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He, Yu-Zhen
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Chou, Chien-Li
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Lee, Suh-Yin
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Lin, Bao-Shuh P.
Self-training plays an important role in sports exercise. However, if not under the instruction of a coach, improper training postures can cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a Yoga self-training system, entitled YogaST, which aims at instructing the user/practitioner to perform the asana (Yoga posture) correctly and preventing injury caused by improper postures. Involving professional Yoga training knowledge, YogaST analyzes the practitioner's posture from both front and side views using two Kinects with perpendicular viewing directions and assists him/her in rectifying bad postures. The contour, skeleton, and feature axes of the human body are extracted as posture representation. Then, YogaST analyzes the practitioner's posture and presents visualized instruction for posture rectification so that the practitioner can easily understand how to adjust his/her posture.
Computer-assisted yoga training system
(2018-09-01)
Chen, Hua-Tsung
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He, Yu-Zhen
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Hsu, Chun-Chieh
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資訊工程學系
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Department of Computer Science
Self-training is essential in sports exercise. However, without the instruction of a coach, a practitioner may progress to a limited extent. Improper postures may even cause serious harm to muscles and ligaments of the body. Hence, the development of computer-assisted self-training systems for sports exercise is a recently emerging research topic. In this paper, we propose a yoga self-training system, which aims at instructing the practitioner to perform yoga poses correctly, assisting in rectifying poor postures, and preventing injury. Integrating computer vision techniques, the proposed system analyzes the practitioner's posture from both front and side views by extracting the body contour, skeleton, dominant axes, and feature points. Then, based on the domain knowledge of yoga training, visualized instructions for posture rectification are presented so that the practitioner can easily understand how to adjust his/her posture. Experiments on twelve yoga poses performed by different practitioners validate the feasibility of the proposed system in yoga training.
Experimental investigation of moist air condensation on hydrophilic, hydrophobic, superhydrophilic, and hybrid hydrophobic-hydrophilic surfaces
(2017-12-01)
Yang, Kai-Shing
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Lin, Kai-Hsiang
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Tu, Cheng-Wei
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He, Yu-Zhen
;
Wang, Chi-Chuan
This study experimentally investigates the condensation performance amid hydrophobic, hydrophilic, superhydrophilic and hydrophobic-hydrophilic hybrid patterned surfaces with air velocity ranging from 0.5 m/s to 4.0 m/s and relative humidity of 85%, 60% and 40%. The hybrid novel surface employs inverted V shape channels design with alternate hydrophilic and hydrophobic channel to direct condensate, and the accumulated condensate is gathered at vertical hydrophilic channel for further effective condensate removal. It is found that the heat transfer coefficient for hydrophobic surface is higher than that of hydrophilic surface irrespective of the operational velocity and relative humidity. Dropwise condensation prevails for the hydrophobic surface and a twig-like structure of condensate is seen for the hydrophilic surface, and this phenomenon becomes more pronounced when the relative humidity is increased. The superhydrophilic surface shows the worst heat transfer performance due to filmwise condensation. The hybrid surface shows superior heat transfer performance over other surfaces. The heat transfer coefficient obtained is around 3-9% higher than that of hydrophobic surface and is about 6-16% higher than hydrophilic one. The proposed novel design offers a shorter cyclic condensate removal time and better condensate drainage. It is found that the maximum diameter.for the hybrid surface is about 80-90% smaller than the hydrophobic surface and the droplet size before falling off is relatively independent of operational velocity. (C) 2017 Elsevier Ltd. All rights reserved.