Exercise Movement Detection Using Spearman Correlation-based Sliding Window Technique
Keywords:
Pose Estimation, Spearman Correlation, Euclidean Distance, Sliding Window, OpenCV, Mediapipe, SciPyAbstract
The lack of an efficient and fast way to track and monitor exercise types and the number of actions completed during workout sessions can lead to incomplete or inaccurate data for fitness enthusiasts, coaches, and healthcare professionals. In this paper, we proposed a system that uses the existing sliding window algorithm; in addition, we modified it with Spearman correlation using machine learning. Also proposed is the developing of a software solution that utilizes advanced algorithms to detect and count the total number of actions completed. Our solution aims to address this issue by providing accurate and comprehensive data, which can be used by fitness enthusiasts, coaches, and healthcare professionals to make informed decisions about workout routines. The proposed solution will provide an innovative and accurate way to track exercise and help individuals achieve their fitness goals. No standard dataset was available for exercise poses, so we created and tested the model on our dataset. The hyperparameter gamma tuned to optimize the classification accuracy on our dataset produced 81%, which is better than other approaches. The experiment results demonstrate the effectiveness of the system in tracking and correcting exercise poses, and its potential to enhance the quality of exercise practice.
Downloads
References
Sun, R. et al. (2022) “Human action recognition using a convolutional neural network based on skeleton heatmaps from two-stage pose estimation”, Biomimetic Intelligence and Robotics, Volume No 2 Issue 3, pp. 100062.Availableat:https://doi.org/10.1016/j.birob.2022.100062.
Chen, K.-Y. et al. (2022) “Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network” Sensors, Volume 22, 5700. https://doi.org/10.3390/s22155700
Ye et.al (2022) “Human action recognition method based on motion excitation and temporal aggregation module”, Heliyon, Volume 8 Issue (11). Available at:
https://doi.org/10.1016/j.heliyon.2022.e11401.
Pavllo et. al. (2019) “3D Human Pose Estimation in Video With Temporal Convolutions and Semi-Supervised Training.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2019.00794
Rogez et. al. (2019). LCR-Net++: “Multi-person 2D and 3D Pose Detection in Natural Images”. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Schober et. al. (2018) “Correlation Coefficients: Appropriate Use and Interpretation”. Anesthesia& Analgesia 126(5):p 1763-1768, May 2018.
N.I. Glumov et. al. (1995) ”Detection of objects on the image using a sliding window mode, Optics & Laser Technology”, Volume 27, Issue 4,1995,Pages 241-249,ISSN0030-3992 https://doi.org/10.1016/0030-3992(95)93752-D.
Song, Liangchen et. al. (2021). “Human pose estimation and its application to action recognition: A survey”, Journal of Visual Communication and Image Representation. 76. 103055. 10.1016/j.jvcir.2021.103055.
Jo, Beomjun et. al. (2022). “Comparative Analysis of OpenPose, Pose Net, and MoveNet Models for Pose Estimation in Mobile Devices”. Traitement du Signal. 39. 119-124. 10.18280/ts.390111.
Mane, D. et. al. (2023), “Smart Yoga Assistant: SVM-based Real-time Pose Detection and Correction System”, International Journal on Recent and Innovation Trends in Computing and Communication, volume 11, Issue 7s, pp. 251–262.
https://doi.org/10.17762/ijritcc.v11i7s.6997
Badiola-Bengoa et. al. (2021) “ A Systematic Review of the Application of Came” Ra-Based Human Pose Estimation in the Field of Sport and Physical Exercise”. Sensors 2021, 21, 5996. https://doi.org/10.3390/s21185996
Sudharshan Chandra Babu et. al.(2019) “A guide to human pose estimation with deep learning”.https://nanonets.com/blog/human-pose-estimation-2d-guide/, 2019.
Andrew Hernandez, Stephen Wright, Yosef Ben-David, Rodrigo Costa, David Botha. Risk Assessment and Management with Machine Learning in Decision Science. Kuwait Journal of Machine Learning, 2(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/196
Sharma, R., Dhabliya, D. A review of automatic irrigation system through IoT (2019) International Journal of Control and Automation, 12 (6 Special Issue), pp. 24-29.
Purnima, T., & Rao, C. K. . (2023). CROD: Context Aware Role based Offensive Detection using NLP/ DL Approaches. International Journal on Recent and Innovation Trends in Computing and Communication, 11(1), 01–11. https://doi.org/10.17762/ijritcc.v11i1.5981
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.