Human Activity Recognition for Healthcare using Smartphones

Abstract

The healthcare benefits associated with regular physical activity monitoring and recognition has been considered in several research studies. Solid evidence shows that regular monitoring and recognition of physical activity can potentially assist to manage and reduce the risk of many diseases such as obesity, cardiovascular and diabetes. A few studies have been carried out in order to develop effective human activity recognition system using smartphone. However, understanding the role of each sensor embedded in the smartphone for activity recognition is essential and need to be investigated. Due to the recent outstanding performance of artificial neural networks in human activity recognition, this work aims to investigate the role of gyroscope and accelerometer sensors and its combination for automatic human activity detection, analysis and recognition using artificial neural networks. The experimental result on the publicly available dataset indicates that each of the sensors can be used for human activity recognition separately. However, accelerometer sensor data performed better than gyroscope sensor data with classification accuracy of 92%. Combining accelerometer and gyroscope performed better than when used individually with an accuracy of 95%.