• 한국체육학회지인문사회과학편
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• v.55 no.6
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• pp.749-757
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• 2016

The purpose of this study was to investigate the effect of stability on one leg standing posture in yoga practice. Thirteen women college student who have never done yoga participated in this study. In order to collect data before and after yoga practicing for two years, we were used 3D motion capture system and electromyography. The results were as follows. First, ranges of motions for Y axis of left knee joint and X axis of right ankle joint were significantly different in dancer posture(p<.05), and then X axis of right ankle and Y axis of left ankle joint were significantly different in tree posture of pre and post training. Second, the planar alignment angle of trunk-pelvis was not significant difference in dancer and tree posture. Third, CoM-distances of Y, Z directions were significant difference in the tree posture(p<.05). Fourth, Muscle activities of both rectus abdominis, erector spinae and left quadriceps were significant difference in tree posture(p<.05). These findings suggested that yoga training played important roles in stable postures as results of decreasing rotation ankle joint and movement of CoM and enforcing core muscles. This study provides evidence for effectiveness of the stability on standing posture and can get a great effect on posture correction by means of yoga training. Hereafter, study on alignment angle, which is a measurement of postural stabilization will be needed by future yoga training.

• Journal of Broadcast Engineering
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• v.26 no.3
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• pp.269-282
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• 2021

As the distribution of 3D content such as augmented reality and virtual reality increases, the importance of real-time computer animation technology is increasing. However, the computer animation process consists mostly of manual or marker-attaching motion capture, which requires a very long time for experienced professionals to obtain realistic images. To solve these problems, animation production systems and algorithms based on deep learning model and sensors have recently emerged. Thus, in this paper, we study four methods of implementing natural human movement in deep learning model and kinect camera-based animation production systems. Each method is chosen considering its environmental characteristics and accuracy. The first method uses a Kinect camera. The second method uses a Kinect camera and a calibration algorithm. The third method uses deep learning model. The fourth method uses deep learning model and kinect. Experiments with the proposed method showed that the fourth method of deep learning model and using the Kinect simultaneously showed the best results compared to other methods.

• Korean Journal of Applied Biomechanics
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• v.19 no.1
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• pp.159-166
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• 2009

The purpose of this study was to develop and automatic gait event detection algorithm using single accelerometer which is attached at the top of the shoe. The sinal vector magnitude and anterior-posterior(x-axis) directional component of accelerometer were used to detect heel strike(HS) and toe off(TO), respectively. To evaluate proposed algorithm, gait event timing was compared with that by force plate and kinematic data. In experiment, 7 subjects performed 10 trials level walking with 3 different walking conditions such as fast, preferred & slow walking. An accelerometer, force plate and 3D motion capture system were used during experiment. Gait event by force plate was used as reference timing. Results showed that gait event by accelerometer is similar to that by force plate. The distribution of differences were spread about $22.33{\pm}17.45m$ for HS and $26.82{\pm}14.78m$ for To and most error was existed consistently prior to 20ms. The difference between gait event by kinematic data and developed algorithm was small. Thus it can be concluded that developed algorithm can be used during outdoor walking experiment. Further study is necessary to extract gait spatial variables by removing gravity factor.