@inproceedings{9b847ad958bb46ef88c75c2b24332040,
title = "An evaluation of user movement data",
abstract = "In this paper, an empirical evaluation of different classification techniques is conducted on user movement data. The datasets used here for experiments are composed of accelerometer data collected from various devices, including smartphones and smart watches. The user movement data was processed and fed into five traditional machine learning algorithms. The classification performances were then compared with a deep learning technique, the Long Short Term Memory-Recurrent Neural Network (LSTM-RNN). LSTM-RNN achieved its highest accuracy at 89\% as opposed to 97\% from a traditional machine learning algorithm, specifically, K-Nearest Neighbors (k-NN), on wrist-worn accelerometer data.",
keywords = "Accelerometer data, Behavioral biometrics, Deep learning, Long short term memory-recurrent neural network, User movement",
author = "Janelle Mason and Christopher Kelley and Bisoye Olaleye and Albert Esterline and Kaushik Roy",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems IEA/AIE 2018 ; Conference date: 25-06-2018 Through 28-06-2018",
year = "2018",
doi = "10.1007/978-3-319-92058-0\_70",
language = "English",
isbn = "9783319920573",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "729--735",
editor = "\{Ait Mohamed\}, Otmane and Malek Mouhoub and Samira Sadaoui and Moonis Ali",
booktitle = "Recent Trends and Future Technology in Applied Intelligence - 31st International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2018, Proceedings",
}