Evolution in Real-Time Automated systems for Personalized Exercise Guidance and Monitoring - Walke, Shreyas; Wadekar, Yash; Ladawa, Aditya; Pandit, Shraddha V; Khopade, Pratik (International Journal of Computer Sciences and Engineering, 2024)
This comprehensive review delves into the dynamic realm of AI-driven fitness assistance and robotic navigation, exploring the evolving challenges and advancements in human pose estimation, fitness assessment, and user engagement during workout sessions. The surveyed studies employ diverse methodologies, spanning from real-time exercise pose identification using OpenCV and MediaPipe to innovative applications like sound localization and deep learning. The paper also explores the integration of robotics in fitness assistance, showcasing systems for social support and personalized workout recommendations. Furthermore, it investigates advancements in robotic navigation, employing both complex and simplified approaches to seamlessly integrate into workout scenarios. This integration aims to provide in-depth workout analysis and accurate guidance to users while autonomously navigating the environment. The convergence of computer vision, machine learning, image processing, and the Internet of Things emerges as a pivotal approach, offering a holistic solution for immersive fitness experiences in both home and gym settings.