Human Activity Prediction Studies Using Wearable Sensors and Machine Learning

Abstract

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.