A Novel Personal Fitness Trainer and Tracker powered by Artificial Intelligence enabled by MEDIAPIPE and OpenCV

Authors

  • Ira Nath JIS College of Engineering, Kalyani – 741235, INDIA
  • Aditya Shaw JIS College of Engineering, Kalyani – 741235, INDIA
  • Aniruddha Bhadra JIS College of Engineering, Kalyani – 741235, INDIA
  • Diya Dey Bhowmick JIS College of Engineering, Kalyani – 741235, INDIA
  • Sayandeep Ghosh JIS College of Engineering, Kalyani – 741235, INDIA
  • Sreeja Chatterjee JIS College of Engineering, Kalyani – 741235, INDIA

Keywords:

Fitness, AI personal trainer, exercise, cost-effective tracking, motion detection

Abstract

Human pose estimation has gained a lot of attention in recent years, and it has become an essential tool in various fields. In order to produce a representation of the human body, such as a body skeleton, from input data, the purpose of human pose estimation is to estimate the locations of human body joints. Additionally, evaluation during workouts and physical therapy is essential to figuring out the best and most appropriate ways to carry out physical activities. This study suggests evaluating bicep curl exercises by measuring the elbow flexion angle and locating critical areas at the shoulder, elbow, and hand using human pose estimation approaches to address this issue. The goal is to assess if the user has obtained the proper amplitude of the exercise by comparing their performance to the standard angle.  We compared our method using the COCO dataset and our own dataset and discovered that the MediaPipe method produced the best results for evaluating bicep curl workouts.

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References

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Published

16.07.2023

How to Cite

Nath, I. ., Shaw, A. ., Bhadra, A. ., Bhowmick, D. D. ., Ghosh, S. ., & Chatterjee, S. . (2023). A Novel Personal Fitness Trainer and Tracker powered by Artificial Intelligence enabled by MEDIAPIPE and OpenCV. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 1085–1094. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3368

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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