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: \(Recall = \frac{tp}{tp + fn}\).
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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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