Enabling advanced inference on sensor nodes through direct use of compressively-sensed signals

Mohammed Shoaib, Niraj Kumar Jha, Naveen Verma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

Nowadays, sensor networks are being used to monitor increasingly complex physical systems, necessitating advanced signal analysis capabilities as well as the ability to handle large amounts of network data. For the first time, we present a methodology to enable advanced decision support on a low-power sensor node through the direct use of compressively-sensed signals in a supervised-learning framework; such signals provide a highly efficient means of representing data in the network, and their direct use overcomes the need for energy-intensive signal reconstruction. Sensor networks for advanced patient monitoring are representative of the complexities involved. We demonstrate our technique on a patient-specific seizure detection algorithm based on electroencephalograph (EEG) sensing. Using data from 21 patients in the CHB-MIT database, our approach demonstrates an overall detection sensitivity, latency, and false alarm rate of 94.70%, 5.83 seconds, and 0.199 per hour, respectively, while achieving data compression by a factor of 10x. This compares well with the state-of-the-art baseline detector with corresponding results being 96.02%, 4.59 seconds, and 0.145 per hour, respectively.

Original languageEnglish (US)
Title of host publicationProceedings - Design, Automation and Test in Europe Conference and Exhibition, DATE 2012
Pages437-442
Number of pages6
StatePublished - May 24 2012
Event15th Design, Automation and Test in Europe Conference and Exhibition, DATE 2012 - Dresden, Germany
Duration: Mar 12 2012Mar 16 2012

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
ISSN (Print)1530-1591

Other

Other15th Design, Automation and Test in Europe Conference and Exhibition, DATE 2012
CountryGermany
CityDresden
Period3/12/123/16/12

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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    Shoaib, M., Jha, N. K., & Verma, N. (2012). Enabling advanced inference on sensor nodes through direct use of compressively-sensed signals. In Proceedings - Design, Automation and Test in Europe Conference and Exhibition, DATE 2012 (pp. 437-442). [6176511] (Proceedings -Design, Automation and Test in Europe, DATE).