RECOGNITION OF WORKOUT EXERCISE BASED ON IMAGE PROCESSING USING CNN MOBILENETV2 AND EFFICIENTNETB3
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Abstract
Exercise is crucial for maintaining a healthy and fit body, yet many individuals struggle to track their workout progress effectively. This paper explores the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), to recognize and classify common exercise moves such as push-ups, pull-ups, sit-ups, leg raises, and squats. The goal is to provide individuals with a tool to assist in counting repetitions and sets, thereby enhancing their exercise experience. The study employs two well-known CNN models, MobileNetV2 and EfficientNetB3, trained on a custom dataset consisting of 2,500 exercise move images. The dataset includes various training-to-testing data ratios, ranging from 90:10 to 50:50. The models are evaluated based on their accuracy in classifying exercise moves, and confusion matrices are generated to analyze their performance further.
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