Tracking user information using motion data through smartphones

In this paper, we use smartphone motion sensors and user basic activities as new source of information to detect and measure some dynamic information of user such as age group, footwear type and the floor surface types, which are important information in human sensing tasks and applications for healthcare, marketing, recommender system and etc. A random forest classifier was used to classify 262features extracted from smart phone accelero-meter and gyroscope sensors' data. Empirical evaluation was performed on a large-scale dataset containing 510 subjects and more than 111470 activities files. The results show that we can achieve accuracies of more than 85%, 92.5% and 95% for the detection of user's age, footwear types and floor surface type respectively.