Author
Listed:
- Dong Fu
- Jian Wang
- Lianhui Li
Abstract
With the rapid development of economy, science, and technology, people’s pursuit of quality of life continues to improve, leisure sports have gradually become a trend, and yoga, as a traditional and fashionable way of fitness, is favored by more and more people. However, the traditional training class yoga teaching time is fixed, which will have many inconveniences for professionals. However, learning according to the teaching video cannot guarantee the accuracy of the movement and may not achieve the training effect. Therefore, in this situation, intelligent e-yoga teaching is of great significance to improve the training level. This system is based on Internet of things technology, selects Kinect as the sensing carrier of human motion information, and designs an electronic yoga teaching system. The system integrates the functions of motion information acquisition and motion evaluation. First, standard yoga movements are collected through Kinect as a comparison template for yoga training. Second, the system collects the trainers’ action data, uses Hausdorff distance algorithm to evaluate the similarity of action flow, and identifies the action name based on the threshold. Third, through the motion evaluation algorithm based on joint point angle measurement, it points out the joint points whose actions are not in place. Based on the completion time of the action, the action speed is evaluated. Finally, the system outputs the evaluation results in the form of text and voice through text conversion technology. The system can carry out efficient posture recognition and can achieve the purpose of evaluating the training quality and giving guiding suggestions. It can meet the basic training needs of users and has great application value.
Suggested Citation
Dong Fu & Jian Wang & Lianhui Li, 2022.
"Design and Application of Yoga Intelligent Teaching Platform Based on Internet of Things,"
Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-8, September.
Handle:
RePEc:hin:jnlmpe:8407408
DOI: 10.1155/2022/8407408
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