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Authors: Abdulrahman Alruban 1 ; Hind Alobaidi 2 ; Nathan Clarke 3 and Fudong Li 4

Affiliations: 1 Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., Computer Sciences and Information Technology College, Majmaah University, Majmaah and Saudi Arabia ; 2 Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., College of Education for Pure Science, University of Baghdad, Baghdad and Iraq ; 3 Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., Security Research Institute, Edith Cowan University Perth Western Australia and Australia ; 4 Centre for Security, Communications and Network Research, Plymouth University, Plymouth, U.K., School of Computing, University of Portsmouth, Portsmouth and U.K.

ISBN: 978-989-758-351-3

Keyword(s): Human Activity Recognition, Smartphone Sensors, Gait Activity, Gyroscope, Accelerometer.

Related Ontology Subjects/Areas/Topics: Ensemble Methods ; Feature Selection and Extraction ; Pattern Recognition ; Theory and Methods

Abstract: Human physical motion activity identification has many potential applications in various fields, such as medical diagnosis, military sensing, sports analysis, and human-computer security interaction. With the recent advances in smartphones and wearable technologies, it has become common for such devices to have embedded motion sensors that are able to sense even small body movements. This study collected human activity data from 60 participants across two different days for a total of six activities recorded by gyroscope and accelerometer sensors in a modern smartphone. The paper investigates to what extent different activities can be identified by utilising machine learning algorithms using approaches such as majority algorithmic voting. More analyses are also provided that reveal which time and frequency domain-based features were best able to identify individuals’ motion activity types. Overall, the proposed approach achieved a classification accuracy of 98% in identifying four dif ferent activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity. (More)

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Paper citation in several formats:
Alruban, A.; Alobaidi, H.; Clarke, N. and Li, F. (2019). Physical Activity Recognition by Utilising Smartphone Sensor Signals.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 342-351. DOI: 10.5220/0007271903420351

@conference{icpram19,
author={Abdulrahman Alruban. and Hind Alobaidi. and Nathan Clarke. and Fudong Li.},
title={Physical Activity Recognition by Utilising Smartphone Sensor Signals},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={342-351},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007271903420351},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Physical Activity Recognition by Utilising Smartphone Sensor Signals
SN - 978-989-758-351-3
AU - Alruban, A.
AU - Alobaidi, H.
AU - Clarke, N.
AU - Li, F.
PY - 2019
SP - 342
EP - 351
DO - 10.5220/0007271903420351

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