Indoors Fitness Training Monitoring based on OpenPose


  • J.Haoran Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan.
  • S. Karungaru Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan.
  • K. Terada Graduate School of Advanced Technology and Science, Tokushima University, Tokushima, Japan.


Image Processing, Deep learning, Body part Recognition


With the continuation of the COVID-19 pandemic, people's daily life has changed. The changing life habits are reflected in the increasing number of hours working at home. Mostly affected is physical fitness, because of limitations or fear of the gym/outdoors or effective exercise indoors.  However, with the arrival of the post-pandemic era, although working at home has improved, the fitness problem still haunts people. Some people have become accustomed to home fitness and are no longer limited to the traditional gym or gymnasium. However, proper and safe exercising is still a challenge due to the lack of live coaching.  With the advent of artificial intelligence and the improvement of virtual reality (VR) and augmented reality (AR) capabilities, the options for live off-site coaching have become feasible. This study is based on OpenPose technology in artificial intelligence to monitor the standard of people's movements in-home fitness. The study results are encouraging.


J. Preece, Y. Rogers, and H. Sharp, "Interaction Design: Beyond Human-Computer Interaction," 4th ed., John Wiley & Sons, 2015.

A. Dix, J. Finlay, G. Abowd, and R. Beale, "Human-Computer Interaction," 3rd ed., Pearson Education, 2003.

Y. Bengio, A. Courville, and P. Vincent, "Representation Learning: A Review and New Perspectives," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.

World Health Organization, "Coronavirus Disease (COVID-19): Situation Report-76," Apr. 2020. [Online]. Available:

R. M. Tintle, "Prevalence of Bacterial Contamination on Gym Equipment," Master's thesis, University of South Florida, Tampa, FL, 2015.

M. K. Goyal, "Emerging Trends in Human-Computer Interaction: Concepts, Methodologies, Tools, and Applications," IGI Global, 2019.

P. Viola and M. Jones, "Rapid Object Detection Using a Boosted Cascade of Simple Features," in Proc. 2001 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR 2001), vol. 1, pp. I-I.

S. J. Pan and Q. Yang, "A Survey on Transfer Learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010.

E. L. van den Broek, "Affective Signal Processing (ASP): Unraveling the Mystery of Emotions," University of Twente, Enschede, The Netherlands, 2011.

J. M. Carroll, Ed., "HCI Models, Theories, and Frameworks: Toward a Multidisciplinary Science," Morgan Kaufmann, 2003.

M. F. Azari, H. K. Yap, and N. A. Bakar, "Interactive Home-based Exercise Coaching for Elderly: A Case Study of Home Care Monitoring System," in Proc. 2017 IEEE 41st Annual Computer Software and Applications Conf. (COMPSAC), vol. 2, pp. 506-511.

M. Billinghurst and T. Starner, "Wearable Devices: New Ways to Manage Information," Computer, vol. 32, no. 1, pp. 57-64, Jan. 1999.

H. Wang, A. Kläser, C. Schmid, and C. Liu, "Dense Trajectories and Motion Boundary Descriptors for Action Recognition," in International Journal of Computer Vision, vol. 103, no. 1, pp. 60-79, 2013.

D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," in International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.

H. Wang and C. Schmid, "Action Recognition with Improved Trajectories," in Proceedings of the IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 2013, pp. 3551-3558.

K. Simonyan and A. Zisserman, "Two-Stream Convolutional Networks for Action Recognition in Videos," in Advances in Neural Information Processing Systems (NIPS), 2014, pp. 568-576.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770-778.

S. Yan, Y. Xiong, and D. Lin, "Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition," in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), New Orleans, Louisiana, USA, 2018, pp. 7442-7452.

T. DeVries and G. W. Taylor, "Improved Regularization of Convolutional Neural Networks with Cutout," arXiv preprint arXiv:1708.04552, 2017.

H. Sakoe and S. Chiba, "Dynamic Programming Algorithm Optimization for Spoken Word Recognition," in IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 26, no. 1, pp. 43-49, 1978.

E. Keogh and M. J. Pazzani, "Derivative Dynamic Time Warping," in Proceedings of the 2001 SIAM International Conference on Data Mining, Chicago, IL, USA, 2001, pp. 1-11.

Y. Du, W. Wang, and L. Wang, "Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2015, pp. 1110-1118.

Z. Cao, T. Simon, S. Wei, and Y. Sheikh, "Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 7291-7299.

L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool, "Temporal Segment Networks: Towards Good Practices for Deep Action Recognition," in Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands, 2016, pp. 20-36.

Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, "Realtime multi-person 2d pose estimation using part affinity fields," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7291–7299.

C. K. Balabanidis, S. K. Michalopoulou, G. N. Koukoulis, and M. I. Panagiotakos, "Wall sit test to evaluate the lower limbs muscle strength and endurance in healthy individuals: reliability and validity study," European Journal of Physical and Rehabilitation Medicine, vol. 56, no. 6, pp. 736-744, 2020.

T. McGill, "The Plank Exercise: Core Strengthening for Fitness and Injury Prevention," Strength and Conditioning Journal, vol. 32, no. 3, pp. 68-74, 2010.

D. Carley, "Plank Exercise: A Core Muscle Workout for Athletes," American Journal of Sports Medicine, vol. 20, no. 5, pp. 72-75, 2013.

F. L. Y. Fung, J. K. H. Ng, and G. Y. F. Ng, "The effect of the plank exercise progression on trunk muscle activity," Physical Therapy in Sport, vol. 45, pp. 90-95, 2020.

S. Kar, S. Chakraborty, and S. Bhattacharya, "Efficient Fitness Action Analysis Based on Spatio-Temporal Feature Encoding," in 2019 IEEE 16th India Council International Conference (INDICON), Rajkot, India, 2019, pp. 1-6.

N. Wiener, "Cybernetics," in Cybernetics: or Control and Communication in the Animal and the Machine, 4th ed., Cambridge, MA: The MIT Press, 1968, pp. 52-57.

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng, "Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations," Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, Quebec, Canada, 2009, pp. 609-616. doi: 10.1145/1553374.1553453.



How to Cite

J.Haoran, S. Karungaru, & K. Terada. (2024). Indoors Fitness Training Monitoring based on OpenPose. JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 6(3). Retrieved from