Hitesh Kotte

This paper introduces IMPECT-POSE, an innovative front-end and back-end architecture designed to enhance fitness and sports training through precise body posture tracking. This system integrates advanced computer vision and artificial intelligence in pose estimation to provide real-time feedback on exercise execution, which is crucial for maintaining proper technique, reducing injury risks, and optimizing training outcomes. Our evaluations, conducted at two distinct locations with multiple participants, demonstrate the system’s capability to improve exercise performance significantly. The system’s flexibility allows sports professionals to monitor and guide clients remotely, enhancing the accessibility and effectiveness of training regimens. This research highlights the potential of augmented intelligence in transforming sports training, offering a scalable and effective alternative to conventional methods, and paving the way for future advancements in AI-driven personalized training programs. The continued development of this technology aims to refine its accuracy, broaden its applicability to diverse user preferences, and extend its use in practical, real-world settings.

Physical fitness presents a significant challenge in ensuring proper exercise posture. Individuals who work out need help maintaining correct exercise posture during their workouts. Maintaining correct form is critical for ensuring the safety and effectiveness of fitness routines. Yet, it is often challenging for individuals to keep proper form without professional guidance, which usually comes at expensive costs. The paper presents a novel method that utilizes the capabilities of YOLOv7 and a primary web camera to offer immediate feedback and correction on body posture during gym activities. Such a method empowers individuals to correct themselves and promotes motivation even without the presence of a professional trainer. This system has been developed to provide immediate, personalized feedback for various fitness exercises. It efficiently counts repetitions and provides textual guidance for improvement, tailored to the specific requirements of fitness enthusiasts. To determine the efficacy of our technology, we carried out a user study in a controlled laboratory setting simulating a gym environment. The study compares our interactive system with the traditional training method, involving participants of varied fitness levels. It showed significant improvements in exercise technique with real-time feedback. These findings are crucial for AI-supported training systems in strength training, underscoring the need for adaptive technologies for different user experiences. The research contributes to human-computer interaction and fitness technology discussions, highlighting interactive models’ potential to augment and sometimes replicate personal training benefits in exercise form and posture improvement.