S.A. Bashir
The effect of window length on accuracy of smartphone-based activity recognition.
Bashir, S.A.; Doolan, D.C.; Petrovski, A.
Abstract
One of the main approaches for personalization of activity recognition is the generation of the classification model from user annotated data on mobile itself. However, giving the resource constraints on such devices there is a need to examine the effects of system parameters such as the feature extraction parameter that can affect the performance of the system. Thus, this paper examines the effects of window length of the sensor data and varying data set sizes on the classification accuracy of four selected supervised machine learning algorithms running on off the shelf smartphone. Our results show that out of the three window lengths of 32, 64 and 128 considered, the 128 window length yields the best accuracy for all the algorithms tested. Also, the time taken to train the algorithms with samples of this length is minimal compare to 64 and 32 window lengths. A smartphone based activity recognition is implemented to utilize the results in an online activity recognition scenario.
Citation
BASHIR, S.A., DOOLAN, D.C. and PETROVSKI, A. 2016. The effect of window length on accuracy of smartphone-based activity recognition. IAENG international journal of computer science [online], 43(1), pages 126-136. Available from: http://www.iaeng.org/IJCS/issues_v43/issue_1/IJCS_43_1_15.pdf
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 29, 2016 |
Online Publication Date | Feb 29, 2016 |
Publication Date | Aug 27, 2016 |
Deposit Date | Dec 19, 2016 |
Publicly Available Date | Dec 19, 2016 |
Journal | IAENG international journal of computer science |
Print ISSN | 1819-656X |
Electronic ISSN | 1819-9224 |
Publisher | IAENG: International Association of Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 43 |
Issue | 1 |
Pages | 126-136 |
Keywords | Activity recognition; Smartphone; Accelerometer sensor data; Machine learning algorithms |
Public URL | http://hdl.handle.net/10059/2043 |
Publisher URL | http://www.iaeng.org/IJCS/issues_v43/issue_1/IJCS_43_1_15.pdf |
Files
BASHIR 2016 Effect of window length
(2.2 Mb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/
You might also like
Beyond vanilla: improved autoencoder-based ensemble in-vehicle intrusion detection system.
(2023)
Journal Article
Advanced persistent threats detection based on deep learning approach.
(2023)
Conference Proceeding
Downloadable Citations
About OpenAIR@RGU
Administrator e-mail: publications@rgu.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search