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IMPECT-POSE: A Complete Front-end and Back-end Architecture for Pose Tracking and Feedback

Abhishek Samanta; Hitesh Kotte; Patrick Handwerk; Khaleel Asyraaf Mat Sanusi; Mai Geisen; Milos Kravcik; Nghia Duong-Trung
In: UMAP Adjunct '24: Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization. International Conference on User Modeling, Adaptation, and Personalization (UMAP-2024), July 1-4, Cagliari, Italy, Pages 142-147, ISBN 979-8-4007-0466-6, ACM, New York, NY, United States, 6/2024.

Abstract

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.

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