Person Identification and Discovery With Wrist Worn Accelerometer Data

Ryan McConville; Raul Santos-Rodriguez; Niall Twomey

Person Identification and Discovery With Wrist Worn Accelerometer Data

Ryan McConville, Raul Santos-Rodriguez, Niall Twomey

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

2 Citations (Scopus)

102 Downloads (Pure)

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.

Original language	English
Title of host publication	Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2018
Pages	615-620
Number of pages	6
Publication status	Published - 22 Mar 2018
Event	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium Duration: 25 Apr 2018 → 27 Apr 2018

Conference

Conference	European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated title	ESANN 2018
Country/Territory	Belgium
City	Bruges
Period	25/04/18 → 27/04/18

Research Groups and Themes

Digital Health
SPHERE

Access to Document

Full-text PDF (final published version)
This is the final published version of the article (version of record). It first appeared online via i6doc.com at https://www.i6doc.com/en/ . Please refer to any applicable terms of use of the publisher.
Final published version, 3.39 MBLicence: CC BY-SA

Handle.net

Persistent link

Cite this

@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 = mar,

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",

note = "European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2018 ; Conference date: 25-04-2018 Through 27-04-2018",

}

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.

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 proceeding › Conference Contribution (Conference Proceeding)

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 (Conference Proceeding)

SN - 9782875870476

SP - 615

EP - 620

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

T2 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 25 April 2018 through 27 April 2018

ER -

Person Identification and Discovery With Wrist Worn Accelerometer Data

Abstract

Conference

Research Groups and Themes

Access to Document

Handle.net

Other files and links

Embargoed Document

Fingerprint

SPHERE (EPSRC IRC)

Cite this

Person Identification and Discovery With Wrist Worn Accelerometer Data

Abstract

Conference

Research Groups and Themes

Access to Document

Handle.net

Other files and links

Embargoed Document

Fingerprint

Projects

SPHERE (EPSRC IRC)

Cite this