NDLI: Context-aware signal processing in medical embedded systems: A dynamic feature selection approach

Content Provider	IEEE Xplore Digital Library
Author	Ghasemzadeh, H. Shirazi, B.
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA (Shirazi, B.) \|\| Comput. Sci. Dept., Univ. of California, Los Angeles, Los Angeles, CA, USA (Ghasemzadeh, H.)
Abstract	Medical embedded systems hold the promise to improve health outcomes, decrease isolation, reduce health disparities, and substantially reduce costs. In spite of their revolutionary potentials, these systems face a number of challenges in design and architecture that form stumbling blocks in their path to success. On one hand, as the sensor units continue to become more miniaturized, the underlying processing architectures demand for further miniaturization and power-efficiency to allow unobtrusive and long-term operation of the system. On the other hand, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytics techniques for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. In this paper, we present a data-processing-driven optimization and information extraction approach to address the problem of dynamic and power-aware feature selection for event classification applications using wearable sensors. Our results show that utilizing contextual information about users can reduce energy consumption of feature extraction module by 72.5% on average, compared to a static feature selection approach.
Starting Page	642
Ending Page	645
File Size	208610
Page Count	4
File Format	PDF
ISBN	9781479902484
DOI	10.1109/GlobalSIP.2013.6736973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Feature Selection Accuracy Body Sensor Networks Signal processing algorithms Feature extraction Approximation algorithms Real-time systems Wearable Computing Partitioning algorithms Signal Processing
Content Type	Text
Resource Type	Article

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