i-manager Publications

Smart Vision-Enabled Real-Time Monitoring for Fitness Freaks with a Feedback System

B. Muthu Senthil *, Uvan Shankar M.**, Tintu Mathew ***, Saranya G. ****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, India.

Periodicity:July - December'2020
DOI : https://doi.org/10.26634/jcc.7.2.18136

Abstract

In the contemporary world, lack of physical fitness is causing several diseases like heart problems, obesity, high blood pressure, diabetes, etc. Anxiety and depression are also major health concerns today. Regular exercise is mandatory for a healthy lifestyle as it combats health conditions and diseases. Proper physical training is essential to meet specific fitness goals and get real benefits. Fitness coaching facilities are expensive and there is a lack of trained professionals. Due to these reasons, people do the exercises themselves. They often get injured due to improper posture and do not get the expected results. This discourages people to do exercise regularly. Our model proposes a virtual physical trainer that can assist users during their exercise. It can provide real-time quality feedback to the users. During exercise, the user's video is recorded and given as input to the pose estimation and machine learning algorithms. After comparing the results with the original exercise models, corrections are sent to the user instantly as audio.

Keywords

Smart-vision, Fitness, Pose Estimation, Machine Learning (ML), Real-Time Feedback, E-health Care, Pre-Trained Models.

How to Cite this Article?

Senthil, B. M., Shankar, M. U., Mathew, T., and Saranya, G. (2020). Smart Vision-Enabled Real-Time Monitoring for Fitness Freaks with a Feedback System. i-manager's Journal on Cloud Computing, 7(2), 1-7. https://doi.org/10.26634/jcc.7.2.18136

References

[1]. Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. J. (2016, October). Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In European Conference on Computer Vision (pp. 561-578). Cham: Springer. https://doi.org/10.1007/978-3-3 19-46454-1_34

[2]. Bourdev, L., & Malik, J. (2009, September). Poselets: Body part detectors trained using 3D human pose annotations. In 2009, IEEE 12^th International Conference on Computer Vision (pp. 1365-1372). IEEE. https://doi.org/ 10.1109/ICCV.2009.5459303

[3]. Buchanan, A., & Fitzgibbon, A. (2006, June). Interactive feature tracking using kd trees and dynamic programming. In 2006, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (Vol. 1, pp. 626-633). IEEE. https://doi.org/10. 1109/CVPR.2006.158

[4]. Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7291-7299).

[5]. Duffner, S., & Garcia, C. (2013). PixelTrack: A fast adaptive algorithm for tracking non-rigid objects. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2480-2487). https://doi.org/10.1109/ ICCV.2013.308

[6]. Han, S. H., Kim, H. G., & Choi, H. J. (2017, February). Rehabilitation posture correction using deep neural network. In 2017, IEEE International Conference on Big Data and Smart Computing (BigComp) (pp. 400-402). IEEE. https://doi.org/10.1109/BIGCOMP.2017.7881743

[7]. Kyaagba, S. (2018, September 7). Dynamic Time Warping with Time Series. Medium. Retrieved from https:// medium.com/@shachiakyaagba_41915/dynamic-timewarping- with-time-series-1f5c05fb8950

[8]. Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014, September). Microsoft COCO: Common objects in context. In European Conference on Computer Vision (pp. 740-755). Cham: Springer. https://doi.org/10.1007/978-3-319-10602-1_48

[9]. Müller, M. (2007). Dynamic time warping. In Information Retrieval for Music and Motion, (pp.69-84). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3- 540-74048-3_4

[10]. Nadeem, A., Jalal, A., & Kim, K. (2020, February). Human actions tracking and recognition based on body parts detection via Artificial neural network. In 2020, 3^rd International Conference on Advancements in Computational Sciences (ICACS) (pp. 1-6). IEEE. https://doi.org/10.1109/ ICACS47775.2020.9055951

[11]. Nagarkoti, A., Teotia, R., Mahale, A. K., & Das, P. K. (2019, July). Realtime indoor workout analysis using machine learning & computer vision. In 2019, 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1440-1443). IEEE. https://doi.org/10.1109/EMBC.2019.8856547

[12]. Parley Labs (2020, January 6). Exploration: Pose Estimation with OpenPose and PoseNet — Parley Labs. Medium. Retrieved from https://parleylabs.medium.com/ exploration-pose-estimation-with-openpose-and-pose net-parley-labs-d7f21b541774

[13]. Toshev, A., & Szegedy, C. (2014). DeepPose: Human pose estimation via deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1653-1660). https://doi.org/10.1109/ CVPR.2014.214

[14]. Zou, J., Li, B., Wang, L., Li, Y., Li, X., Lei, R., & Sun, S. (2018, November). Intelligent fitness trainer system based on human pose estimation. In International Conference on Signal and Information Processing, Networking and Computers (pp. 593-599). Springer, Singapore. https://doi. org/10.1007/978-981-13-7123-3_69

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Smart Vision-Enabled Real-Time Monitoring for Fitness Freaks with a Feedback System

Abstract

Keywords

How to Cite this Article?

References

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