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Designing and prototyping of AI-based real-time mobile detectors for calisthenic push-up exercise
Citation
Zhang, X., Han, S. Z. H., & Lim, K. Y. T. (2024). Designing and prototyping of AI-based real-time mobile detectors for calisthenic push-up exercise. Procedia Computer Science, 239, 445–452. https://doi.org/10.1016/j.procs.2024.06.192
Abstract
Fitness exercises, including push-ups, are very beneficial to personal health. Many Artificial Intelligence (AI)-based fitness trainers are developed based on human pose estimation models or assisted by Internet of Things (IoT) devices. However, many of them require access to a graphing processing unit (GPU) for model training or IoT sensors to deploy, less accessible for individuals. In our work, we designed and prototyped real-time mobile push-up detectors using three distinctive approaches: (1) Push-up pose classification, (2) Angle-heuristic estimation and (3) Optical flow detection. We trained our deep-learning model with over 2000 images to achieve a high accuracy for real time deployment. Models are tested on our video dataset applied data augmentation techniques to simulate real-world environmental conditions to evaluate model performance based on accuracy metrics (precision, recall, F1 score) and processing frame rate (FPS). From the results, we concluded that the angle-heuristic estimation method has the best overall performance and we analysed the reasons for the relatively poorer performance of the push-up pose classification and optical flow detection methods. All methods developed are capable of working on mobile devices without the need of GPU or IoT sensors.
Date Issued
2024
Publisher
Elsevier
Journal
Procedia Computer Science
DOI
10.1016/j.procs.2024.06.192