Ready-to-use activity recognition for smartphones

In this study, every day activities are recognized from data collected using smartphones accelerometer sensors. Offline experiments are made to show that the presented method is user- and body position-independent. In addition, it is shown that the features used in the classification are not dependent on the calibration of the phone. The recognition models trained using the offline data are also tested online. A mobile application running these models is built for two operating systems: Symbian^3 and Android. Real-time experiments using these applications are made to show that the presented method can be implemented to any operating system and hardware variations do not affect recognition results. High recognition accuracies are obtained, in the offline study, the average recognition rate is almost 99% and, also, in the online study, the average recognition accuracy is over 90%.