@inproceedings{bbb15da7878944f6a31867f994b6e198,
title = "Energy-efficient signal processing in wearable embedded systems: An optimal feature selection approach",
abstract = "Many wearable embedded systems benefit from classification algorithms where statistical features extracted from physiological signals are mapped onto different user's states such as health status of a patient or type of activity performed by a subject. Conventionally selected features lead to rapid battery depletion in these battery-operated systems, mainly due to the absence of computing complexity criterion while selecting prominent features. In this paper, we introduce the notion of power-aware feature selection, which minimizes energy consumption of the signal processing for classification applications. Our approach takes into consideration the energy cost of individual features that are calculated in real-time. The problem is formulated using integer programming and a greedy approximation is presented to select the features in a power-efficient manner. Experimental results on thirty channels of activity data demonstrate that our approach can significantly reduce energy consumption of the computing module resulting in more than 30$% energy savings while achieving 96.7% classification accuracy.",
keywords = "activity recognition, embedded signal processing, feature selection, power optimization, wearable monitoring",
author = "Hassan Ghasemzadeh and Navid Amini and Majid Sarrafzadeh",
year = "2012",
doi = "10.1145/2333660.2333739",
language = "English (US)",
isbn = "9781450312493",
series = "Proceedings of the International Symposium on Low Power Electronics and Design",
pages = "357--362",
booktitle = "ISLPED'12 - Proceedings of the International Symposium on Low Power Electronics and Design",
note = "2012 ACM/IEEE International Symposium on Low Power Electronics and Design, ISLPED'12 ; Conference date: 30-07-2012 Through 01-08-2012",
}