Wearable-Based Human Activity Recognition Using an IoT Approach

Abstract

This paper presents a novel system based on the Internet of Things (IoT) to Human Activity Recognition (HAR) by monitoring vital signs remotely. We use machine learning algorithms to determine the activity done within four pre-established categories (lie, sit, walk and jog). Meanwhile, it is able to give feedback during and after the activity is performed, using a remote monitoring component with remote visualization and programmable alarms. This system was successfully implemented with a 95.83% success ratio.

Keywords

• e-health
• human activity recognition (HAR)
• Internet of Things (IoT)
• rule tree classifier
• C4.5
• Bayesian classifier