Revamping the Workout Routine: An Overview of PoseNet Thunder-Driven Fitness Apps Incorporating Computer Vision and Machine Learning

Abstract

In today's modern lifestyle, characterised by a sedentary routine, the prevalence of chronic diseases among individuals has increased significantly. Juggling a 9-5 job, family responsibilities, and social commitments leaves little time for commuting to a gym regularly. As a result, more people are opting to exercise at home, leveraging the convenience of having fitness resources available through their handheld devices. This shift towards home workouts is facilitated by the remarkable advancements in technology. Using Different innovative approaches leverage the power of deep learning algorithms to analyse exercise routines and classify different types of workouts. Through computer vision, the system can process visual information from workout videos, enabling the recognition and understanding of various body movements, postures, and exercise techniques. This provides users with valuable insights and feedback on their performance, ensuring that they are executing exercises correctly and effectively. The integration of deep learning, computer vision, and image processing technologies offers immense potential in the field of exercise analysis and classification. Consistently performing an exercise improperly may result in significant long-term injury. We propose a method to assess the user's body posture during a workout and compare it to a professional's reference workout to assist address this problem and provide visual feedback while conducting a workout. To detect faults and deliver remedial action to the user, we model the human body as a collection of limbs and assess angles between limb pairs. Our system builds on the latest advancements using deep learning for human body pose estimation.