The work "Machine learning methods in Smartphone-Based Activity Recognition" has been published with the support of NEANIAS project.

• SACI 2020 - IEEE 14th International Symposium on Applied Computational Intelligence and Informatics - May 21-23, 2020 - Timişoara, Romania.

Abstract

In this paper, we present a system for human physical Activity Recognition (AR) using smartphone with embedded sensors. This paper addresses the question whether there is a comfortable way to predict human activities based on collected data from smartphone embedded gyroscope and accelerometer. Computational background of this work based on self-learning machine learning methods. In order to train the machine learning algorithms, The University of California, Irvine (UCI) dataset was used and the different models were compared. After selecting the best model further modifications were suggested in order to improve the accuracy of the model. At the end 96.88% accuracy was reached.

Acknowledgements

The presented work was partially funded by the European H2020 NEANIAS project under grant No. 863448, and by the Hungarian Scientific Research Fund (OTKA) under project No. 132838. We thank for the usage of MTA Cloud (https://cloud.mta.hu/) that significantly helped us achieving the results published in this paper.