TY - GEN
T1 - An optimal approach for low-power migraine prediction models in the state-of-the-art wireless monitoring devices
AU - Pagan, Josue
AU - Fallahzadeh, Ramin
AU - Ghasemzadeh, Hassan
AU - Moya, Jose M.
AU - Risco-Martin, Jose L.
AU - Ayala, Jose L.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/11
Y1 - 2017/5/11
N2 - Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with limited battery autonomy. Several researchers focus their efforts in reducing the energy consumption of these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption of monitoring devices for the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of several sources of energy consumption: the running code for prediction, and the sensors for data acquisition. As a result of our methodology, we are able to improve the energy consumption of the computing process up to 90% with a minimal impact on accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.
AB - Wearable monitoring devices for ubiquitous health care are becoming a reality that has to deal with limited battery autonomy. Several researchers focus their efforts in reducing the energy consumption of these motes: from efficient micro-architectures, to on-node data processing techniques. In this paper we focus in the optimization of the energy consumption of monitoring devices for the prediction of symptomatic events in chronic diseases in real time. To do this, we have developed an optimization methodology that incorporates information of several sources of energy consumption: the running code for prediction, and the sensors for data acquisition. As a result of our methodology, we are able to improve the energy consumption of the computing process up to 90% with a minimal impact on accuracy. The proposed optimization methodology can be applied to any prediction modeling scheme to introduce the concept of energy efficiency. In this work we test the framework using Grammatical Evolutionary algorithms in the prediction of chronic migraines.
UR - http://www.scopus.com/inward/record.url?scp=85020193413&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020193413&partnerID=8YFLogxK
U2 - 10.23919/DATE.2017.7927193
DO - 10.23919/DATE.2017.7927193
M3 - Conference contribution
AN - SCOPUS:85020193413
T3 - Proceedings of the 2017 Design, Automation and Test in Europe, DATE 2017
SP - 1297
EP - 1302
BT - Proceedings of the 2017 Design, Automation and Test in Europe, DATE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th Design, Automation and Test in Europe, DATE 2017
Y2 - 27 March 2017 through 31 March 2017
ER -