Abstract
It is a well-known proverb that a healthy mind lives in a healthy body, and yoga is one such means for connecting the body to the mind. However, yoga should be performed under professional supervision and in a regulated manner, as it can be harmful to one's health if done incorrectly. Moreover, it is difficult for beginners to identify the incorrect portions of their yoga postures on their own. In this research article, we present a user-friendly python-flask based web application that assists its registered users to perform every pose accurately. We have used computer vision techniques as it can perform various visual data frame related operations in real time. Our method consists of two main components: a hand gesture component that records video using hand gestures and a pose estimation component that detects body joint coordinates. The system then compares the angles obtained from the instructor's pose and the users for feedback generation and provides correction if the difference is larger than a certain threshold. With this inherent capability of pose feedback generation, the proposed system thus enables the naive performers to evaluate their poses and correct it when it deviates from the correct pose sequence. The method was evaluated in real-time on people of varied age groups and gender for four different asanas, and it was proven that it recognizes incorrect portions of the performed asanas for all the test cases. The experimental findings in terms of feedback generated using the user videos gave a functional validation of the proposed procedure and its usability in modern day human life.
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References
Guddeti RR, Dang G, Williams MA, Alla VM (2019) Role of Yoga in cardiac disease and rehabilitation. J Cardiopulm Rehabil Prev 3:146–152
Rodríguez-Hidalgo AJ, Pantaleón Y, Dios I, Falla D (2020) Fear of COVID-19, Stress, and Anxiety in University undergraduate students: a predictive model for depression. Front Psychol 11
Sharma YK, Sharma S, Sharma E (2018) Scientific benefits of Yoga: a review. Int J Multidiscip Res 03:11–148
Chen Y, Tian Y, He M (2020) Monocular human pose estimation: a survey of deep learning-based methods. Comput Vis Image Understand
Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, Zhang F, Chang CL, Yong MG, Lee J, Chang WT, Hua W, Georg M, Grundmann M (2019) MediaPipe: a framework for building perception pipelines
Yoga: Its Origin, History and Development: https://www.mea.gov.in/search-result.htm?25096/Yoga:_su_origen,_historia_y_desarrollo#:~:text=The%20word%20'Yoga'%20is%20derived,and%20body%2C%20Man%20%26%20Nature. Accessed 2021
Chen HT, He YZ, Hsu CC (2018) Computer-assisted yoga training system. Multimed Tools Appl 77:23969–23991
Trejo EW, Yuan P (2018) Recognition of Yoga poses through an interactive system with kinect device. In: 2018 2nd international conference robotics and automation science: ICRAS, pp 12–17
Borkar PK, Pulinthitha MM, Pansare A (2019) Match pose—a system for comparing poses. Int J Eng Res Technol (IJERT) 08(10)
Rishan F, Silva BB, Alawathugoda S, Nijabdeen S, Rupasinghe L, Liyanapathirana C (2020) Infinity Yoga Tutor: Yoga posture detection and correction system. In: 2020 5th international conference on information technology research
Cao Z, Simon T, Wei SE, Sheikh Y (2017) OpenPose: realtime multi-person 2D pose estimation using part affinity fields. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 7291–7299
Islam MU, Mahmud H, Ashraf FB, Hossain I, Hasan MK (2017) Yoga posture recognition by detecting human joint points in real time using microsoft Kinect. In: 2017 IEEE region 10 humanitarian technology conference (R10-HTC), Dhaka, pp 668–673
Zhang F, Bazarevsky V, Vakunov A, Tkachenka A, Sung G, Chang CL, Grundmann G (2021) MediaPipe hands: on-device real-time hand tracking
Telles S, Sharma SK, Chetry D, Balkrishna A (2021) Benefits and adverse effects associated with yoga practice: a cross-sectional survey from India. Complementary therapies in medicine. Elsevier
MediaPipe Github. https://google.github.io/mediapipe/solutions/hands. Accessed 2021
On-device, Real-time Body Pose Tracking with MediaPipe BlazePose. https://ai.googleblog.com/2020/08/on-device-real-time-body-pose-tracking.html. Accessed 2021
MediaPipe Github. https://google.github.io/mediapipe/solutions/pose. Accessed 2021
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Sachdeva, R., Maheshwari, I., Maan, V., Sangwan, K.S., Prakash, C., Dhiraj (2023). A Computer Vision Assisted Yoga Trainer for a Naive Performer by Using Human Joint Detection. In: Muthusamy, H., Botzheim, J., Nayak, R. (eds) Robotics, Control and Computer Vision. Lecture Notes in Electrical Engineering, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-99-0236-1_30
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DOI: https://doi.org/10.1007/978-981-99-0236-1_30
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