Review Article Open Access

Human Activity Prediction Studies Using Wearable Sensors and Machine Learning

Divya Sharma1 and Usha Chauhan1
  • 1 Department of Electrical, Electronics and Communication Engineering, Galgotias University, Greater Noida, India


Nowadays, Human Activity Recognition (HAR) systems have become so demanding that they find applications in the field of assisted living systems, elderly healthcare systems, smart homes, healthcare monitoring applications, surveillance systems, etc. Due to its increasing demand in computer science; machine learning and deep learning have brought a paradigm shift in the area of sensor-based recognition systems. Providing accurate information about individuals is essential in pervasive computing, but human activity detection is challenging due to the complexity and speed of activities, dynamic recording requirements, and diverse application areas. This survey aims to identify the best wearable device and most optimal machine learning algorithms for HAR in terms of classification accuracy, as well as analyze which algorithms are suitable for specific application areas. The recent advances in HAR systems through machine learning and deep learning techniques have been discussed and from this analysis, it has been observed that CNN and RNN-LSTM techniques have achieved maximum classification efficiency of 92-95.78% on the ADL dataset.

Journal of Computer Science
Volume 20 No. 4, 2024, 431-441


Submitted On: 2 October 2023 Published On: 14 February 2024

How to Cite: Sharma, D. & Chauhan, U. (2024). Human Activity Prediction Studies Using Wearable Sensors and Machine Learning. Journal of Computer Science, 20(4), 431-441.

  • 0 Citations



  • Activities of Daily Living (ADL)
  • Machine Learning
  • Wearable Technology