Enhancing Human Activity Recognition through Machine Learning Models: A Comparative Study

Authors

  • Katragadda Megha Shyam Department of Computer Science & Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India
  • Sindhura Surapaneni Department of Computer Science and Engineering, NRI Institute of Technology, Agiripalli, India
  • Pulletikurthy Dedeepya Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, India
  • N Sampreet Chowdary Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, India
  • Balamuralikrishna Thati Department of CSE, Dhanekula Institute of Engineering & Technology. Ganguru,Vijayawada, India

DOI:

https://doi.org/10.15157/IJITIS.2025.8.1.258-271

Keywords:

Accelerometer, Human-Computer Interaction, Censor Data, Recognizing Human Activity, Gesture recognition, Pattern Recognition, Real-Time Monitoring

Abstract

This study explores Human Activity Recognition (HAR), a machine learning technique utilized in health monitoring and human-computer interaction. HAR identifies human actions through sensor data from accelerometers and gyroscopes in smartphones and wearables. Key components of this technique include model selection, feature extraction, preprocessing, and data collection to classify activities such as standing, lying, sitting, and walking. Despite its potential, privacy concerns warrant further research for effective deployment. A comprehensive analysis of HAR techniques has been described in this research work.

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Published

2025-03-10

How to Cite

Shyam, K. M., Surapaneni, S., Dedeepya, P., Chowdary, N. S., & Thati, B. (2025). Enhancing Human Activity Recognition through Machine Learning Models: A Comparative Study. International Journal of Innovative Technology and Interdisciplinary Sciences, 8(1), 258–271. https://doi.org/10.15157/IJITIS.2025.8.1.258-271