Combining Accelerometer and Gyroscope Data in Smartphone-Based Activity Recognition using Movelets

Abstract

Physical activity patterns can be informative about a patient's health status. Traditionally, activity data have been gathered using patient self-report. However, these subjective data can suffer from bias and are difficult to collect over long time periods. Smartphones offer an opportunity to address these challenges. The smartphone has built-in sensors that can be programmed to collect data objectively, unobtrusively, and continuously. Due to their widespread adoption, smartphones are also accessible to most of the population. A main challenge in smartphone-based activity recognition is extracting information optimally from multiple sensors to identify the unique features of different activities. In our study, we analyze data collected by the accelerometer and gyroscope, which measure the phone's acceleration and angular velocity, respectively. We propose an extension to the "movelet method" that jointly incorporates both sensors. We also apply this joint-sensor method to a data set we collected previously. The findings show that combining data from the two sensors can result in more accurate activity recognition than using each sensor alone. For example, the joint-sensor method reduces errors of the gyroscope-only method in differentiating between standing and sitting. It also reduces errors of the accelerometer-only method in classifying vigorous activities.