From User-independent to Personal Human Activity Recognition Models Using Smartphone Sensors

In this study, a novel method to obtain user-dependent human activity recognition models unobtrusively by using the sensors of a smartphone is presented. The recognition consists of two models: sensor fusion-based user-independent model for data labeling and single sensor-based userdependent model for final recognition. The functioning of the presented method is tested with human activity data set, including data from accelerometer and magnetometer, and with two classifiers. Comparison of the detection accuracies of the proposed method to traditional userindependent model shows that the presented method has potential, when the method is tested with two classifiers and five persons, in nine cases out of ten it is better than the traditional method, but more experiments using different sensor combinations should be made to show the full potential of the method. 1 Problem statement and related work Smartphones have become increasingly common and people carry them everywhere they go, this means that nowadays almost every one has a device capable to be used to recognize activities. This of course enables a lot of possilities to the field of activity recognition. As known, smartphones include a wide range of sensors, such as magnetometer, gyroscope, GPS, proximity sensor, ambient light sensor, thermometer and barometer. Still, most activity recognition studies use only accelerometers in detection, for instance [1], [2], [3], [4]. However, by fusing the sensors of a mobile phone more accurate models could be build than using only one sensor [5]. On the other hand, this improvement comes at the expense of energy efficiency. While smartphones have a limited battery capacity, the sensor fusion-based recognition is seldom a usable approach when the aim is to monitor everyday life 24/7. This calls for innovative solutions to employ sensor fusion. For instance, in [6] a smart and energy-efficient way to deploy the sensors of a mobile phone to recognize activities was presented. The method presented in the study uses the minimum number of sensors needed to detect user’s activity reliably and when activity changes, more sensors are used to detect the new activity. By using this type of smart sensor selection, the battery life can be improved by 75%. In addition, in [7] the idea to use sensor fusion to build adaptive recognition models was presented. In the study, a method 471 ESANN 2016 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), 27-29 April 2016, i6doc.com publ., ISBN 978-287587027-8. Available from http://www.i6doc.com/en/.