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An Adaptive Sampling System for Sensor Nodes in Body Area Networks

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2 Author(s)
Robert Rieger ; Electr. Eng. Dept., Nat. Sun Yat-Sen Univ., Kaohsiung ; John T. Taylor

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.

Published in:

IEEE Transactions on Neural Systems and Rehabilitation Engineering  (Volume:17 ,  Issue: 2 )