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Person Identification and Discovery With Wrist Worn Accelerometer Data

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

Standard

Person Identification and Discovery With Wrist Worn Accelerometer Data. / McConville, Ryan; Santos-Rodriguez, Raul; Twomey, Niall.

Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018. 2018. p. 615-620.

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

Harvard

McConville, R, Santos-Rodriguez, R & Twomey, N 2018, Person Identification and Discovery With Wrist Worn Accelerometer Data. in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018. pp. 615-620, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 25/04/18.

APA

McConville, R., Santos-Rodriguez, R., & Twomey, N. (2018). Person Identification and Discovery With Wrist Worn Accelerometer Data. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018 (pp. 615-620)

Vancouver

McConville R, Santos-Rodriguez R, Twomey N. Person Identification and Discovery With Wrist Worn Accelerometer Data. In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018. 2018. p. 615-620

Author

McConville, Ryan ; Santos-Rodriguez, Raul ; Twomey, Niall. / Person Identification and Discovery With Wrist Worn Accelerometer Data. Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018. 2018. pp. 615-620

Bibtex

@inproceedings{41bcc3e72fc14c7c91a5115b7681e744,
title = "Person Identification and Discovery With Wrist Worn Accelerometer Data",
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 classificationand 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.",
author = "Ryan McConville and Raul Santos-Rodriguez and Niall Twomey",
year = "2018",
month = "3",
day = "22",
language = "English",
isbn = "9782875870476",
pages = "615--620",
booktitle = "Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018",

}

RIS - suitable for import to EndNote

TY - GEN

T1 - Person Identification and Discovery With Wrist Worn Accelerometer Data

AU - McConville, Ryan

AU - Santos-Rodriguez, Raul

AU - Twomey, Niall

PY - 2018/3/22

Y1 - 2018/3/22

N2 - 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 classificationand 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.

AB - 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 classificationand 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.

UR - https://www.i6doc.com/en/book/?GCOI=28001100176760#h2tabtableContents

M3 - Conference contribution

SN - 9782875870476

SP - 615

EP - 620

BT - Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018

ER -