From Pixels to Perfect Form: Deep Learning for Real-time Yoga Pose Analysis
Keywords:
LSTM, Multi-Layer Perceptron, Speed Up Robust Feature (SURF), Yoga Pose, Deep LearningAbstract
For maintaining a healthy life, yoga plays a very important role. Now, a day's people are using online platforms to learn yoga poses and maintain a healthy life. While doing yoga the most important point to take care of is maintaining the correct yoga posture. To learn how to recognize suitable yoga poses and give feedback to improve posture, this research presents a deep learning-based method for estimating yoga posture. The model is period-ically fed frames from videos or pictures. Key points are extracted with Keras multi-person pose estimation, yielding 12 joint vectors. The angles of these vectors relative to the x-axis are calculated. One of the six yoga positions is identified by applying a classification model based on these angles. A dataset with six distinct yoga poses, resulting in 70 videos and 350 instances, is used to test the methods. There are thirty validation examples, three hundred training instances, and thirty testing instances in the dataset. Various approaches such as Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and CNN in conjunction with Long Short-Term Memory (CNN + LSTM) are implemented, and results reveals a competitive performance mosaic. Though CNN and CNN + LSTM architectures are believed to be superior, the updated feature set allows Multi-Layer Perceptron (MLP) to achieve an im-pressive accuracy of 0.9958
References
Aritz Badiola-Bengoa and Amaia Mendez-Zorrilla. A systematic review of the application of camera-based human pose estimation in the field of sport and physical exercise. Sensors, 21(18):5996, 2021
Varsha Bhole and Arun Kumar. A transfer learning-based approach to predict the shelf life of fruit. Inteligencia Artificial, 24(67):102–120, 2021
David Chang. Natural Language Processing and Graph Representation Learning for Clinical Data. PhD thesis, Yale University, 2021
Raviteja R Guddeti, Geetanjali Dang, Mark A Williams, and Venkata Mahesh Alla. Role of yoga in cardiac disease and rehabilitation. Journal of cardiopulmonary rehabilitation and prevention, 39(3):146– 152, 2019
Shrajal Jain, Aditya Rustagi, Sumeet Saurav, Ravi Saini, and Sanjay Singh. Three-dimensional CNN-inspired deep learning architecture for yoga pose recognition in the real-world environment. Neural Computing and Applications, 33:6427–6441, 2021
D Mohan Kishore, S Bindu, and Nandi Krishnamurthy Manjunath. Estimation of yoga postures using machine learning techniques. International Journal of Yoga, 15(2):137–143, 2022
Yinsong Liu, Junsheng Yu, and Hanlin Mou. Photoplethysmography- based cuffless blood pressure estimation: an image encoding and fusion approach. Physiological Measurement, 44(12):125004, 2023
Zhiqiang Luo, Weiting Yang, Zhong Qiang Ding, Lili Liu, I-Ming Chen, Song Huat Yeo, Keck Voon Ling, and Henry Been-Lirn Duh. “left arm up!” interactive yoga training in virtual environment. In 2011 IEEE virtual reality conference, pages 261–262. IEEE, 2011
Siddharth Patil, Amey Pawar, Aditya Peshave, Aamir N Ansari, and Arundhati Navada. Yoga tutor visualization and analysis using surf algorithm. In 2011 IEEE control and system graduate research colloquium, pages 43–46. IEEE, 2011
Ronald Poppe. A survey on vision-based human action recognition. Image and vision computing, 28(6):976–990, 2010
Fazil Rishan, Binali De Silva, Sasmini Alawathugoda, Shakeel Ni- jabdeen, Lakmal Rupasinghe, and Chethana Li-yanapathirana. Infinity yoga tutor: Yoga posture detection and correction system. In 2020 5th International conference on information technology research (ICITR), pages 1–6. IEEE, 2020
Gopinath Sathyanarayanan, Ashvini Vengadavaradan, and Balaji Bharad- waj. Role of yoga and mindfulness in severe mental illnesses: A narrative review. International journal of yoga, 12(1):3–28, 2019
Sumeet Saurav, Prashant Gidde, and Sanjay Singh. Exploration of deep learning architectures for real-time yoga pose recognition. Multimedia Tools and Applications, pages 1–43, 2024
Marc B Schure, John Christopher, and Suzanne Christopher. Mind–body medicine and the art of self-care: teaching mindfulness to counseling students through yoga, meditation, and qigong. Journal of Counseling & Development, 86(1):47–56, 2008
Mayank Singh, Pawan K Gupta, Vipin Tyagi, Jan Flusser, Tuncer O¨ ren, and Rekha Kashyap. Advances in Compu-ting and Data Sciences: Third International Conference, ICACDS 2019, Ghaziabad, India, April 12– 13, 2019, Revised Selected Papers, Part II, volume 1046. Springer, 2019
Vivek Anand Thoutam, Anugrah Srivastava, Tapas Badal, Vipul Kumar Mishra, GR Sinha, Aditi Sakalle, Harshit Bhardwaj, and Manish Raj. Yoga pose estimation and feedback generation using deep learning. Computational Intelli-gence and Neuroscience, 2022, 2022
Aman Upadhyay, Niha Kamal Basha, and Balasundaram Ananthakr- ishnan. Deep learning-based yoga posture recognition using the y pn- mssd model for yoga practitioners. In Healthcare, volume 11, page 609. MDPI, 2023
Santosh Kumar Yadav, Amitojdeep Singh, Abhishek Gupta, and Jagdish Lal Raheja. Real-time yoga recognition using deep learning. Neural computing and applications, 31:9349–9361, 2019
https://www.kaggle.com/datasets/niharika41298/yoga-poses-dataset?resource=download
