A Comparative Analysis of the Impact of Features on Human Activity Recognition with Smartphone Sensors

Abstract

The recognition of users' physical activities through data analysis of smartphone inertial sensors has aided the development of several solutions in different domains such as transportation and healthcare. Mostly of these solutions have been supported by the cloud communication technologies due to the need of using accurate classification models. In an attempt to solve problems related to the smartphone orientation (e.g. landscape) in the user's body, new types of features classified as orientation independent have arisen in the last years. In this context, this paper presents an extensive comparative study between all the features mapped in literature derived from inertial sensors. A number of experiments were carried out using two databases containing data from 30 users. Results showed that the new orientation independent features proposed in literature cannot discriminate properly between the users' activities using the inertial sensors. In addition, this paper provides an extensive analysis of these type of features and a tool that implements all methodological process of human activity recognition based on smartphones.