An empirical evaluation of activities and classifiers for user identification on smartphones

In the past few years, smart mobile devices have become ubiquitous. Most of these devices have embedded sensors such as GPS, accelerometer, gyroscope, etc. There is a growing trend to use these sensors for user identification and activity recognition. Most prior work, however, contains results on a small number of classifiers, data, or activities. We present a comprehensive evaluation often representative classifiers used in identification on two publicly available data sets (thus our work is reproducible). Our results include data obtained from dynamic activities, such as walking and running; static postures such as sitting and standing; and an aggregate of activities that combine dynamic, static, and postural transitions, such as sit-to-stand or stand-to-sit. Our identification results on aggregate data include both labelled and unlabeled activities. Our results show that the k-Nearest Neighbors algorithm consistently outperforms other classifiers. We also show that by extracting appropriate features and using appropriate classifiers, static and aggregate activities can be used for user identification. We posit that this work will serve as a resource and a benchmark for the selection and evaluation of classification algorithms for activity based identification on smartphones.