Exercise Movement Detection Using Spearman Correlation-based Sliding Window Technique - Kaulage, Anant; Mane, Deepak; Upadhye, Gopal; Rajput, Satpalsing D.; Kale, Satish; Zope , Bhushan (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.