Muscle Imbalance Detection and Solution

Abstract

Muscle imbalance is a pervasive issue, particularly among athletes, particularly newcomers to the realm of bodybuilding. It arises from various factors, including incorrect movement angles, injuries, and other factors. Identifying muscle imbalances manually is challenging, as subtle differences may escape the naked eye. To address this challenge, we’ve developed a mobile application that performs muscle identification and classification. The system goes beyond mere classification; it leverages a trained model to predict potential imbalances. When an imbalance is detected within a specific muscle group, the system offers tailored exercise recommendations to rectify the issue. Conversely, balanced muscle groups receive a "balanced" designation. Our innovative system employs Deep Learning Algorithms, specifically utilizing "CNN" (Convolutional Neural Networks) and "TensorFlow" models, including "MobileNet SSD v2." This not only enables the system to identify imbalanced muscles but also provides comprehensive exercise recommendations for rectifying these imbalances. Furthermore, our application features modules for login validation and management, muscle classification, and solution recommendations tailored to different muscle groups. The model, which achieved a 90% testing accuracy, is currently designed for image-based results. In future iterations, we aim to enhance the system by incorporating the capability to identify imbalances using live video via a device’s camera. Additionally, we plan to introduce exercise plans and training tips, catering to beginners in the field of bodybuilding.