A Comprehensive Review on Human Activity and Fitness Tracker using Different Approaches
Yashraj Mishra · International Journal for Research in Applied Science and Engineering Technology · 2025
The increasing demand for accurate and real-time human activity and fitness tracking has led to the development of diverse computer vision and deep learning models. Among these, OpenPose has emerged as a powerful tool for multi-person 2D pose estimation, enabling precise body posture tracking from RGB images. This review paper presents a comprehensive analysis of state-of-the-art approaches for human activity recognition and posture detection, with a primary focus on comparing OpenPose with other convolutional neural network (CNN)-based architectures currently used in academic research and commercial applications. We investigate the strengths and limitations of different models in terms of detection accuracy, computational efficiency, robustness in dynamic environments, and application in fitness and healthcare systems. The paper consolidates findings from 30 IEEE research publications, highlighting how various approaches have evolved and been implemented for body posture recognition, real-time fitness feedback, and rehabilitation monitoring. Additionally, we discuss the integration of these models with wearable sensors and mobile applications, their performance in real-world scenarios, and future research directions aiming to improve usability, personalization, and energy efficiency. This review provides valuable insights for researchers and developers seeking to advance human activity tracking through deep learning-based posture estimation techniques.