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Person Identification and Discovery With Wrist Worn Accelerometer Data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationProceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018
Pages615-620
Number of pages6
DateAccepted/In press - 25 Jan 2018
DatePublished (current) - 22 Mar 2018
EventEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 25 Apr 201827 Apr 2018

Conference

ConferenceEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN 2018
CountryBelgium
CityBruges
Period25/04/1827/04/18

Abstract

Internet of Things (IoT) devices with embedded accelerometers continue to grow in popularity. These are often attached to individuals, whether they are a mobile phone in a pocket or a smartwatch on a wrist, and are constantly capturing data of a personal nature. In this work we propose a method for person identification using accelerometer data via supervised machine learning techniques. Further, we introduce the first unsupervised method for discovering individuals using the same accelerometer. We report the performance both in terms of classification
and clustering using a publicly available dataset covering a large number of activities of daily living. While this has numerous benefits in tasks such as activity recognition and biometrics, this work also motivates the debate and discussion around privacy concerns of the analysis of accelerometer data.

    Structured keywords

  • Digital Health

Event

European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Abbreviated titleESANN 2018
Duration25 Apr 201827 Apr 2018
CityBruges
CountryBelgium
Degree of recognitionInternational event

Event: Conference

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    Licence: CC BY-SA

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