@inproceedings{274eaa284a384407a28ae9b9934c2812,
title = "Subject recognition using wrist-worn triaxial accelerometer data",
abstract = "This study demonstrates how a subject can be identified by the means of accelerometer data generated through wrist-worn devices in the context of clinical trials where data integrity is of utmost importance. A custom vector of features extracted from the daily accelerometer time series is defined. Feature selection is adapted to take account of the sequential structure in features. Several classifiers are compared within three different learning frameworks: binary, multi-class and one-class. A simple algorithm like logistic regression shows excellent performance in the binary and multi-class frameworks.",
keywords = "Accelerometer data, Anomaly detection, Classification, Clinical trials",
author = "Stefano Mauceri and Louis Smith and James Sweeney and James McDermott",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2018.; 3rd International Conference on Machine Learning, Optimization, and Big Data, MOD 2017 ; Conference date: 14-09-2017 Through 17-09-2017",
year = "2018",
doi = "10.1007/978-3-319-72926-8_48",
language = "English",
isbn = "9783319729251",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "574--585",
editor = "Giuseppe Nicosia and Giovanni Giuffrida and Panos Pardalos and Renato Umeton",
booktitle = "Machine Learning, Optimization, and Big Data - Third International Conference, MOD 2017, Revised Selected Papers",
}