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An Adaptive Sampling System for Sensor Nodes in Body Area Networks


Reference:

Rieger, R. and Taylor, J. T., 2009. An Adaptive Sampling System for Sensor Nodes in Body Area Networks. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17 (2), pp. 183-189.

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    Official URL:

    http://dx.doi.org/10.1109/tnsre.2008.2008648

    Abstract

    The importance of body sensor networks to monitor patients over a prolonged period of time has increased with an advance in home healthcare applications. Sensor nodes need to operate with very low-power consumption and under the constraint of limited memory capacity. Therefore, it is wasteful to digitize the sensor signal at a constant sample rate, given that the frequency contents of the signals vary with time. Adaptive sampling is established as a practical method to reduce the sample data volume. In this paper a low-power analog system is proposed, which adjusts the converter clock rate to perform a peak-picking algorithm on the second derivative of the input signal. The presented implementation does not require an analog-to-digital converter or a digital processor in the sample selection process. The criteria for selecting a suitable detection threshold are discussed, so that the maximum sampling error can be limited. A circuit level implementation is presented. Measured results exhibit a significant reduction in the average sample frequency and data rate of over 50% and 38%, respectively.

    Details

    Item Type Articles
    CreatorsRieger, R.and Taylor, J. T.
    DOI10.1109/tnsre.2008.2008648
    Uncontrolled Keywordsanalog electronics, bio-signal recording, adaptive sampling, sensor networks, analog signal processing
    DepartmentsFaculty of Engineering & Design > Electronic & Electrical Engineering
    Research CentresCentre for Advanced Sensor Technologies (CAST)
    Publisher Statementreiger-17-2-2009.pdf: Copyright © 2009 IEEE. Reprinted from IEEE Transactions on Neural Systems and Rehabilitation Engineering. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Bath’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
    RefereedYes
    StatusPublished
    ID Code14262

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