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International Conference on Human-Computer Interaction

HCII 2022: Human-Computer Interaction. Technological Innovation pp 81–91Cite as

A Self Learning Yoga Monitoring System Based on Pose Estimation

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13303))

Abstract

Modern life is stressful - long working hours, poor diets, inactivity, and increasing social isolation in the digital age have all contributed to rising rates of anxiety and depression. The COVID-19 pandemic escalated this situation with a series of quarantines. Without physical interaction, learning a new skill can be frustrating and stressful, especially a skill like Yoga that requires balance and physical coordination. On the one hand, many people cannot afford a personal trainer, but on the other, books and video tutorials do not offer personalized feedback. These limitations make learning Yoga on our own an overwhelming task. We propose a minimal investment model that will help people learn and practice correct Yoga forms from the comfort of their homes in an easy, stress-free manner - just by using their camera. A deep learning model is proposed based on PoseNet which gives an accuracy of 96.77% on the test dataset. We also conducted a survey to measure the satisfaction, efficiency and effectiveness of our system. Overall, 81.8% of the participants felt that our system helped them learn and perform the exercises better and 52.2% of them rated it as being “very good”.

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Notes

  1. 1.

    https://blog.tensorflow.org/2018/05/real-time-human-pose-estimation-in.html.

  2. 2.

    https://blog.tensorflow.org/2018/05/real-time-human-pose-estimation-in.html.

  3. 3.

    https://ml5js.org/.

  4. 4.

    https://p5js.org/.

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Authors and Affiliations

  1. Computer Vision Group, Department of Computer Science and Engineering, Indian Institute of Information Technology, Sri City, Chittoor, India

    Prahitha Movva, Hemanth Pasupuleti & Himangshu Sarma

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  1. Prahitha Movva
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  2. Hemanth Pasupuleti
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  3. Himangshu Sarma
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Corresponding author

Correspondence to Himangshu Sarma .

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Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

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Movva, P., Pasupuleti, H., Sarma, H. (2022). A Self Learning Yoga Monitoring System Based on Pose Estimation. In: Kurosu, M. (eds) Human-Computer Interaction. Technological Innovation. HCII 2022. Lecture Notes in Computer Science, vol 13303. Springer, Cham. https://doi.org/10.1007/978-3-031-05409-9_7

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  • DOI: https://doi.org/10.1007/978-3-031-05409-9_7

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