NDLI: Energy-efficient signal processing in wearable embedded systems: an optimal feature selection approach

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Energy-efficient signal processing in wearable embedded systems: an optimal feature selection approach

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Energy-efficient signal processing in wearable embedded systems: an optimal feature selection approach

Content Provider	ACM Digital Library
Author	Amini, Navid Sarrafzadeh, Majid Ghasemzadeh, Hassan
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.
Starting Page	357
Ending Page	362
Page Count	6
File Format	PDF
ISBN	9781450312493
DOI	10.1145/2333660.2333739
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-07-30
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Feature selection Power optimization Wearable monitoring Activity recognition Embedded signal processing
Content Type	Text
Resource Type	Article

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