An Adaptive Sampling System for Sensor Nodes in Body Area Networks

R Rieger, John T Taylor

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33 Citations (SciVal)
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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.
Original languageEnglish
Pages (from-to)183-189
Number of pages7
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number2
Publication statusPublished - 2009


  • analog electronics
  • bio-signal recording
  • Adaptive sampling
  • sensor networks
  • analog signal processing


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