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Evolution in Real-Time Automated systems for Personalized Exercise Guidance and Monitoring

Shreyas Walke1 , Yash Wadekar2 , Aditya Ladawa3 , Pratik Khopade4 , Shraddha V. Pandit5

  1. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  2. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  3. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  4. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.
  5. Dept. of Artificial Intelligence and Data Science, PES’s Modern College of Engineering, India.

Section:Review Paper, Product Type: Journal Paper
Volume-12 , Issue-3 , Page no. 30-36, Mar-2024

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v12i3.3036

Online published on Mar 31, 2024

Copyright © Shreyas Walke, Yash Wadekar, Aditya Ladawa, Pratik Khopade, Shraddha V. Pandit . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Shreyas Walke, Yash Wadekar, Aditya Ladawa, Pratik Khopade, Shraddha V. Pandit, “Evolution in Real-Time Automated systems for Personalized Exercise Guidance and Monitoring,” International Journal of Computer Sciences and Engineering, Vol.12, Issue.3, pp.30-36, 2024.

MLA Style Citation: Shreyas Walke, Yash Wadekar, Aditya Ladawa, Pratik Khopade, Shraddha V. Pandit "Evolution in Real-Time Automated systems for Personalized Exercise Guidance and Monitoring." International Journal of Computer Sciences and Engineering 12.3 (2024): 30-36.

APA Style Citation: Shreyas Walke, Yash Wadekar, Aditya Ladawa, Pratik Khopade, Shraddha V. Pandit, (2024). Evolution in Real-Time Automated systems for Personalized Exercise Guidance and Monitoring. International Journal of Computer Sciences and Engineering, 12(3), 30-36.

BibTex Style Citation:
@article{Walke_2024,
author = {Shreyas Walke, Yash Wadekar, Aditya Ladawa, Pratik Khopade, Shraddha V. Pandit},
title = {Evolution in Real-Time Automated systems for Personalized Exercise Guidance and Monitoring},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2024},
volume = {12},
Issue = {3},
month = {3},
year = {2024},
issn = {2347-2693},
pages = {30-36},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5671},
doi = {https://doi.org/10.26438/ijcse/v12i3.3036}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v12i3.3036}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5671
TI - Evolution in Real-Time Automated systems for Personalized Exercise Guidance and Monitoring
T2 - International Journal of Computer Sciences and Engineering
AU - Shreyas Walke, Yash Wadekar, Aditya Ladawa, Pratik Khopade, Shraddha V. Pandit
PY - 2024
DA - 2024/03/31
PB - IJCSE, Indore, INDIA
SP - 30-36
IS - 3
VL - 12
SN - 2347-2693
ER -

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Abstract

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.

Key-Words / Index Term

Autonomous Robot Navigation, Human Pose Estimation, Mediapipe, Computer Vision, Deep Learning

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