A Comprehensive Review on Human Activity and Fitness Tracker using Different Approaches

Abstract

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.

Introduction

1. Overview

Recent advancements in AI and computer vision have transformed human activity recognition (HAR) and fitness tracking, with significant applications in healthcare, sports, rehabilitation, and human-computer interaction. Central to these developments is accurate human posture estimation, particularly through frameworks like OpenPose and CNN-based models.

2. Key Technologies and Methodologies

A. OpenPose

• A real-time 2D multi-person pose estimation framework.

• Uses Part Affinity Fields (PAFs) and confidence maps to locate body keypoints from RGB images.

• Pros:

  • Supports multiple people.

  • High spatial accuracy.

• Cons:

  • High GPU demand; not ideal for edge deployment.

B. CNN-Based HAR

• Uses convolutional neural networks to classify human activities based on video or sensor data (e.g., accelerometers, gyroscopes).

• Often combined with temporal models (e.g., LSTMs, GRUs).

• Pros:

  • Automatic feature extraction.

  • High accuracy with structured data.

• Cons:

  • Complex models not suitable for lightweight or real-time systems.

C. Lightweight Models for Edge Devices

• Optimized for mobile and embedded deployment.

• Use lightweight CNN architectures like MobileNet, SqueezeNet, and EfficientNet-lite.

• Techniques: quantization, pruning, knowledge distillation.

• Pros:

  • Energy-efficient, real-time, privacy-preserving.

  • Works offline.

• Cons:

  • Lower accuracy compared to full models.

3. Comparative Analysis

4. Discussion

• OpenPose excels in spatial posture estimation but is resource-heavy.

• CNN-based models are highly effective in recognizing activities from temporal data but may lack real-time edge compatibility.

• Sensor-based HAR (e.g., DeepSense, HAR-Net) offers privacy-aware and energy-efficient solutions, especially suitable for wearables.

• Edge AI (e.g., TinyPose, MobileNet) is gaining traction for offline, low-latency, and private inference.

• Major challenges remain:

  • Occlusion, variable lighting, and clutter.

  • Domain generalization and personalization.

  • Ethical concerns: data privacy, secure handling, and user consent.

5. Future Directions

1. Multimodal Sensor Fusion: Combining video, motion sensors, EEG, ECG, and GPS for more accurate and context-aware recognition.

2. Edge Optimization: Continue developing real-time, low-power models (quantization, pruning, etc.).

3. Personalized Models: Employ federated learning and continual learning for adaptive, privacy-respecting HAR systems.

Conclusion

Human activity and posture recognition has emerged as a critical area in computer vision and AI, offering wide-ranging applications in healthcare, fitness tracking, surveillance, assistive technology, and human-computer interaction. This review explored a broad spectrum of approaches used in this domain, with a specific focus on comparing traditional Convolutional Neural Network (CNN)-based methods and cutting-edge frameworks such as OpenPose and MediaPipe. Through a critical analysis of 30 IEEE research papers, we evaluated the methodological advances, performance benchmarks, and trade-offs across various models. The integration of MediaPipe in your own AI-powered human activity tracker demonstrates the capabilities of lightweight and real-time frameworks in solving real-world problems, particularly in mobile and embedded environments. These innovations have significantly reduced computational complexity while maintaining high accuracy in pose detection and activity classification. However, challenges remain, including the need for generalization in unconstrained environments, privacy preservation, and the integration of multiple data modalities for deeper contextual understanding. Furthermore, there is a growing need to move toward personalized and explainable AI systems that can adapt to individual users while providing transparency in decision-making processes. As research continues to progress, the field is poised for impactful advancements—especially in combining 3D pose estimation, sensor fusion, and real-time analytics on edge devices. The future of human activity tracking lies in scalable, efficient, and human-centric solutions that can seamlessly integrate into daily life, empowering users with actionable insights and fostering better health, safety, and productivity outcomes. From this comparative study, it is evident that: 1) OpenPose offers high accuracy for multi-person setups but isn\'t practical for real-time mobile use. 2) CNN-based HAR works well with sensor data and is suitable for structured datasets. 3) MediaPipe and BlazePose provide a balanced trade-off between speed and accuracy on smartphones. 4) Lightweight models shine in low-resource environments and embedded systems but may need ensembling for accuracy boosts.

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Copyright

Copyright © 2025 Yashraj Mishra, Ankita Jaiswal, Anany Shukla, Abhishek Verma, Humesh Verma, Dr. Goldi Soni. 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.