Real-Time Biomechanical Squat and Deadlift Posture Analysis Using Google Machine Learning Kit

Abstract: This project presents the development of a mobile application for real-time posture analysis during squat and deadlift exercises, using Google Machine Learning (ML) Kit pose detection. Proper exercise form is critical in preventing injuries, underscoring the need for systems that provide immediate feedback, an aspect often missing in existing fitness applications. This study addresses that gap by designing an app that not only guides users through motion analysis but also incorporates a safety mechanism to detect sudden falls. The system employs algorithms to process landmarks, calculate joint angles, count repetitions, and trigger emergency alerts. Two groups of bodybuilders confirmed the usability and accuracy in real-time biomechanical squat and deadlift posture analysis. These findings contribute to the field of AI-driven fitness by introducing a non-wearable, mobile-based solution for guided strength training. In addition, it offers societal benefits as an AI-powered fitness coach that aims to promote public health.

Liew Yee Jie, Ting Tin Tin, Chaw Jun Kit, Ali Aitizaz, Ayodeji Olalekan Salau, Omolayo M. Ikumapayi and Lim Siew Mooi. “Real-Time Biomechanical Squat and Deadlift Posture Analysis Using Google Machine Learning Kit”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.9 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160952

@article{Jie2025,
title = {Real-Time Biomechanical Squat and Deadlift Posture Analysis Using Google Machine Learning Kit},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160952},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160952},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {9},
author = {Liew Yee Jie and Ting Tin Tin and Chaw Jun Kit and Ali Aitizaz and Ayodeji Olalekan Salau and Omolayo M. Ikumapayi and Lim Siew Mooi}
}