Identifying Users with Wearable Sensors based on Activity Patterns

Conference paper


Ehatisham-ul-Haq, M., Malik, M. N., Azam, M. A., Naeem, U., Khalid, A. and Ghazanfar, M. 2020. Identifying Users with Wearable Sensors based on Activity Patterns. The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020). Madeira, Portugal 02 - 05 Nov 2020 Elsevier. https://doi.org/10.1016/j.procs.2020.10.005
AuthorsEhatisham-ul-Haq, M., Malik, M. N., Azam, M. A., Naeem, U., Khalid, A. and Ghazanfar, M.
TypeConference paper
Abstract

We live in a world where ubiquitous systems surround us in the form of automated homes, smart appliances and wearable devices. These ubiquitous systems not only enhance productivity but can also provide assistance given a variety of different scenarios. However, these systems are vulnerable to the risk of unauthorized access, hence the ability to authenticate the end-user seamlessly and securely is important. This paper presents an approach for user identification given the physical activity patterns captured using on-body wearable sensors, such as accelerometer, gyroscope, and magnetometer. Three machine learning classifiers have been used to discover the activity patterns of users given the data captured from wearable sensors. The recognition results prove that the proposed scheme can effectively recognize a user’s identity based on his/her daily living physical activity patterns.

Year2020
ConferenceThe 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020)
PublisherElsevier
Publisher's version
License
File Access Level
Anyone
Publication dates
Online11 Nov 2020
Publication process dates
Accepted02 Aug 2020
Deposited16 Nov 2020
JournalProcedia Computer Science
Journal citation117, pp. 8-15
ISSN1877-0509
Digital Object Identifier (DOI)https://doi.org/10.1016/j.procs.2020.10.005
Copyright holder© 2020 The Authors
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