Abstract
Studying human motion from images and videos has turned into an interesting topic of research given the recent advances in computer vision and deep learning algorithms. When focusing on the automatic procedure of tracking physical exercises, cameras can be used for full human pose estimation in relation to worn sensors. In this work, we propose a method for workout repetition counting and validation based on a set of skeleton-based and deep semantic features that are obtained from a 2D human pose estimation network. Given that some of the individuals’ body parts might be occluded throughout physical exercises, we also perform a multi-view analysis on supporting cameras to improve our recognition rates. Nevertheless, the obtained results for a single-view approach show that we are able to count valid repetitions with over \(90\%\) precision scores for 4 out of 5 considered exercises, while recognizing more than \(50\%\) of the invalid ones.
B. Ferreira and P. M. Ferreira—Both authors contributed equally to this work.
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Notes
- 1.
Demo: https://youtu.be/zu5p0eZUEsQ.
- 2.
From [5] – Precision is the fraction of relevant instances among the retrieved instances: \(Precision = \frac{tp}{tp+fp}\). Recall measures the proportion of actual positives that are correctly identified: \(Recall = \frac{tp}{tp + fn}\).
References
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. arXiv preprint arXiv:1812.08008 (2018)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995). https://doi.org/10.1007/BF00994018
Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)
Kent, A., Berry, M.M., Luehrs Jr., F.U., Perry, J.W.: Machine literature searching viii. Operational criteria for designing information retrieval systems. Am. Doc. 6(2), 93–101 (1955)
Khurana, R., Ahuja, K., Yu, Z., Mankoff, J., Harrison, C., Goel, M.: Gymcam: detecting, recognizing and tracking simultaneous exercises in unconstrained scenes. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 4, pp. 1–17 (2018)
Rêgo, M.L., Cabral, D.A., Costa, E.C., Fontes, E.B.: Physical exercise for individuals with hypertension: it is time to emphasize its benefits on the brain and cognition. Clin. Med. Insights: Cardiol. 13, 1179546819839411 (2019)
Skawinski, K., Montraveta Roca, F., Findling, R.D., Sigg, S.: Workout type recognition and repetition counting with CNNs from 3D acceleration sensed on the chest. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11506, pp. 347–359. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20521-8_29
Soro, A., Brunner, G., Tanner, S., Wattenhofer, R.: Recognition and repetition counting for complex physical exercises with deep learning. Sensors 19(3), 714 (2019)
Åkerberg, A., Soderlund, A., Lindén, M.: Technologies for physical activity self-monitoring: a study of differences between users and non-users. Open Access J. Sports Med. 8, 17–26 (2017). https://doi.org/10.2147/OAJSM.S124542
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Ferreira, B. et al. (2020). Exploring Workout Repetition Counting and Validation Through Deep Learning. In: Campilho, A., Karray, F., Wang, Z. (eds) Image Analysis and Recognition. ICIAR 2020. Lecture Notes in Computer Science(), vol 12131. Springer, Cham. https://doi.org/10.1007/978-3-030-50347-5_1
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