Skip to main content
SpringerLink
Log in

BeFit—A Real-Time Workout Analyzer

  • Conference paper
  • First Online:
Sentimental Analysis and Deep Learning

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1408))

Abstract

Maintaining one’s physical fitness is of utmost importance. Exercising regularly is very important as it helps improve the quality of life. However, incorrect posture during exercises may lead to severe long-term injuries such as back pain, Tendinitis or even hamstring strains. Hence, this application BeFit is proposed that analyzes the posture of the user performing a particular workout by comparing their workout to the reference image or video provided by the system. The system will analyze the angles between the limbs of the body and compare it to the reference video or image using the Cosine rule. After synchronizing user and reference image or video the system gives a green skeleton if the user posture is correct and a red skeleton if the user posture is incorrect. This model has been achieved using the PoseNet library on Tensorflow. The maximum score that the PoseNet model achieves ranges from 0.92874 to 0.98325 for all the key points. With the help of this model, fitness enthusiasts can perform a particular workout accurately at the comfort of their home without getting injured and with proper guidance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Times of India. “64 per cent Indians don’t exercise: Study—Times of India.” The Times of India, July 3, 2019. https://timesofindia.indiatimes.com/life-style/health-fitness/health-news/64-per-cent-indians-dont-exercise-study/articleshow/70038656.cms

  2. CSCS, Andrew Heffernan. “7 common exercises you’re doing wrong-and how to fix them.” Openfit, August 29, 2020. www.openfit.com/common-exercises-wrong-strength

  3. Nagarkoti, A., Teotia, R., Mahale, A., & Das, P. (2019). Realtime indoor workout analysis using machine learning & Computer vision. Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society (pp. 1440–1443). https://doi.org/10.1109/EMBC.2019.8856547

  4. Yadav, S., Singh, A., Gupta, A., & Raheja, J. (2019). Real-time yoga recognition using deep learning. Neural Computing and Applications, 31. https://doi.org/10.1007/s00521-019. https://doi.org/10.1007/s00521-019-04232-7.

  5. Kumar, D., & Sinha, A. (2020). Yoga pose detection and classification using deep learning. International Journal of Scientific Research in Computer Science Engineering and Information Technology. https://doi.org/10.32628/CSEIT206623

  6. Markolefas, F., Moirogiorgou, K., Giakos, G., & Zervakis, M. (2018). Virtual video synthesis for personalized training (pp. 1–6). https://doi.org/10.1109/IST.2018.8577097

  7. Borkar, P. K., Pulinthitha, M. M., & Mrs. Pansare, A. (2019). Match pose—A system for comparing poses, International Journal of Engineering Research & Technology (IJERT), 08(10) (2019, October).

    Google Scholar 

  8. Mehta, D., Sotnychenko, O., Mueller, F., Xu, W., Elgharib, M., Fua, P., Seidel, H.-P., Rhodin, H., Pons-Moll, G., & Theobalt, C. (2020, July). XNect: Real-time multi-person 3D motion capture with a single RGB camera. ACM Transactions on Graphics 39. 4, 82, 17 pages. https://doi.org/10.1145/3386569.3392410

  9. Ding, W., Hu, B., Liu, H., et al. (2020). Human posture recognition based on multiple features and rule learning. International Journal of Machine Learning and Cybernetics, 11, 2529–2540. https://doi.org/10.1007/s13042-020-01138-y

    Article  Google Scholar 

  10. Vyas, P. (2019). “Pose estimation and action recognition in sports and fitness” Master’s Projects. 695. https://doi.org/10.31979/etd.w8ug-4v5c

  11. H. Xiong, S. Berkovsky, Sharan R. V., Liu S., & Coiera, E. (2020) “Robust vision-based workout analysis using diversified deep latent variable model,” 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2155–2158). https://doi.org/10.1109/EMBC44109.2020.9175454

  12. Cao, Z., Simon, T., Wei, S., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 1302–1310. https://doi.org/10.1109/CVPR.2017.143

    Article  Google Scholar 

  13. Rishan, F., de Silva, B., Alawathugoda, S., Nijabdeen, S., Rupasinghe, L., & Liyanapathirana, C. (2020) “Infinity yoga tutor: Yoga posture detection and correction system.” 2020 5th International Conference on Information Technology Research (ICITR) (pp. 1–6). https://doi.org/10.1109/ICITR51448.2020.9310832

  14. Chao, Y.-W., Yang, J., Price, B., Cohen, S., & Deng, J. (2017). Forecasting human dynamics from static images (pp. 3643–3651). https://doi.org/10.1109/CVPR.2017.388

  15. Park, S., Hwang, J., & Kwak, N. (2016). 3D Human pose estimation using convolutional neural networks with 2D pose information. https://doi.org/10.1007/978-3-319-49409-8_15

  16. Fani, M., Neher, H., Clausi, D. A., Wong, A., & Zelek, J. (2017). Hockey action recognition via integrated stacked hourglass network. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017, 85–93. https://doi.org/10.1109/CVPRW.2017.17

    Article  Google Scholar 

  17. Sawant, C. (2020, January 2). Human activity recognition with openpose and Long Short-Term Memory on real time images. EasyChair Home Page. https://easychair.org/publications/preprint/gmWL

  18. Posenet Library. https://github.com/tensorflow/tfjs-models/tree/master/posenet

  19. Tensorflow Blog, “Real-time Human Pose Estimation in the Browser with TensorFlow.js,” https://medium.com/tensorflow/real-time-human-pose-estimation-in-the-browser-with-tensorflow-js-7dd0bc881cd5

  20. Description of 17 key-points detected by PoseNet, Tensorflow Blog

    Google Scholar 

  21. Track Human Poses in Real-Time on Android with TensorFlow Lite. The TensorFlow Blog, https://blog.tensorflow.org/2019/08/track-human-poses-in-real-time-on-android-tensorflow-lite.html

  22. Hidalgo, G., Sheikh, Y., Kitani, K., Bansal, A., Sanabria R., Xiang, D., Li, X., & Idrees, H., (2019). OpenPose: Whole-body pose estimation.

    Google Scholar 

  23. Brown, V. (2021). The bike fit with Todd—Part two. Nicholas Brown. https://nichollasbrown.wordpress.com/2020/05/26/the-bike-fit-with-todd-part-two/

  24. Sin and Cosine rules objectives: Calculate missing sides and angles is non-right angles triangles. (2021). Ppt Download. https://slideplayer.com/slide/6955412/

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering VESIT, Mumbai, India

    Richard Joseph, Manoj Ayyappan, Tanvi Shetty, Gurudatt Gaonkar & Aashish Nagpal

Authors
  1. Richard Joseph
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Manoj Ayyappan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tanvi Shetty
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gurudatt Gaonkar
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aashish Nagpal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manoj Ayyappan .

Editor information

Editors and Affiliations

  1. Institute of Engineering, Tribhuvan University, Pulchowk Campus, Lalitpur, Nepal

    Subarna Shakya

  2. Intelligent Systems Research Centre, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Songkla University, Songkhla, Thailand

    Sinchai Kamolphiwong

  4. Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada

    Ke-Lin Du

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Joseph, R., Ayyappan, M., Shetty, T., Gaonkar, G., Nagpal, A. (2022). BeFit—A Real-Time Workout Analyzer. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, KL. (eds) Sentimental Analysis and Deep Learning. Advances in Intelligent Systems and Computing, vol 1408. Springer, Singapore. https://doi.org/10.1007/978-981-16-5157-1_24

Download citation

Publish with us

Policies and ethics

Search

Navigation