Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification

Abstract

In recent years, the need to monitor health using sensors integrated on popular smart devices is receiving attention. The development of the human activity classification (HAR) system allowed the monitoring and assessing human health status. Most research in this area has been done on smartphones with the limitation of a fixed position on the body to collect raw data and combine it with other machine learning algorithms to improve activity classification performance. However, the phone’s location on the body in many studies was not the same, leading to different data collection. Smartwatches solved this problem because they were worn on the human hand and had stability and sensitivity to the body’s activities. This research would evaluate the accuracy using data from accelerometers on smartphones and smartwatches, combining with some machine learning algorithms to classify four activities: sitting, standing, walking, and jogging. The classification performance was evaluated through accuracy, sensitivity, and specificity. The overall results showed that the data from the smartwatches accelerometer had higher accuracy than data from smartwatches.