Classification of Physical Activities Using a Smartphone: Evaluation Study Using Multiple Users

Nowadays, smartphones play an ubiquitous role in accessing and processing information, most of them having a myriad of integrated sensors that makes them capable of generating information with high accuracy and precision. The monitoring of physical exercises presents itself as one of the new trends, made possible by the use of devices like smartphones. Motion sensors such as the accelerometer enable live motion measurement. This paper intends to study this issue and develop an application for the Android operating system, which takes advantage of sensors embedded in smartphones and web technologies, with the goal to classify multiple physical activities. The developed solution is based on client-server architecture. The client application performs data acquisition, visualization and recording of the signal obtained by the smartphone's accelerometer and the server application receives the information acquired by the client, processes it and classifies it. In order for the application to be able to classify multiple movements throughout the activity performed by the user, an extensive analysis of the acquired signals was carried out to understand their most distinctive features. We used a supervised approach with the goal of reviewing the best techniques that should be useful for achieving the classification with the lowest error. For the signals acquisition the smartphone was positioned along the waist, inside the right front pocket in an attempt to simulate conditions as naturally as possible. The study explored features extracted in both the time and frequency domain, and parametric and non-parametric classifiers. Preliminary results demonstrate that the classification of activities can be done with remarkable accuracy (> 95%).