Physical Activity Recognition by Utilising Smartphone Sensor Signals

Abdulrahman Alruban, Hind Alobaidi, Nathan Clarke, Fudong Li

2019

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 different activities: walking, walking upstairs, walking downstairs, and sitting (on a chair) while the subject is calm and doing a typical desk-based activity.

Paper Citation

in Harvard Style

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

in Bibtex Style

@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},
}

in EndNote Style

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