Prediction of time series using ARMA models in an energy-efficient body area network
Author | Heurtefeux, Karel |
Author | Mohsin, Nasreen |
Author | Menouar, Hamid |
Author | Abuali, Najah |
Available date | 2025-10-16T07:06:41Z |
Publication Date | 2015-01-20 |
Publication Name | Proceedings of the 2014 4th International Conference on Wireless Mobile Communication and Healthcare Transforming Healthcare Through Innovations in Mobile and Wireless Technologies Mobihealth 2014 |
Identifier | http://dx.doi.org/10.1109/MOBIHEALTH.2014.7015953 |
Citation | K. Heurtefeux, N. Mohsin, H. Menouar and N. AbuAli, "Prediction of time series using ARMA models in an energy-efficient body area network," 2014 4th International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare Through Innovations in Mobile and Wireless Technologies (MOBIHEALTH), Athens, Greece, 2014, pp. 230-233, doi: 10.4108/Mobihealth33544.2014.7015953. |
Abstract | This paper investigates the tradeoff between accuracy and complexity cost to predict electrocardiogram values using auto-regressive moving average (ARMA) models in a fully functional body area network (BAN) platform. The proposed BAN platform captures, processes, and wirelessly transmits six-degrees-of-freedom inertial and electrocardiogram data in a wearable, non-invasive form factor. To reduce the number of packets sent, ARMA models are used to predict electrocardiogram (ECG) values. However, in the context of wearable devices, where the computing and memory capabilities are limited, the prediction model should be both accurate and lightweight. To this end, the goodness of the ARMA parameters is quantified considering ECG signal, we compute Akaike Information Criterion (AIC) on more than 900000 ECG measures. Finally, a tradeoff is given accordingly to the hardware constraints. |
Sponsor | This work was made possible by NPRP grant #NPRP4-553–2-210 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | IEEE |
Subject | Akaike Autoregressive moving average Body area network Energy efficiency |
Type | Conference |
Pagination | 230-233 |
EISBN | 978-1-63190-014-3 |
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QMIC Research [307 items ]