DocumentCode :
1231734
Title :
An Adaptive Sampling System for Sensor Nodes in Body Area Networks
Author :
Rieger, Robert ; Taylor, John T.
Author_Institution :
Electr. Eng. Dept., Nat. Sun Yat-Sen Univ., Kaohsiung
Volume :
17
Issue :
2
fYear :
2009
fDate :
4/1/2009 12:00:00 AM
Firstpage :
183
Lastpage :
189
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.
Keywords :
adaptive signal processing; body area networks; electrocardiography; health care; medical signal processing; patient monitoring; signal sampling; adaptive sampling system; body area networks; circuit level implementation; home healthcare applications; low-power analog system; patient monitoring; peak-picking algorithm; sensor nodes; Adaptive systems; Body area networks; Body sensor networks; Capacitive sensors; Frequency; Medical services; Memory management; Patient monitoring; Sampling methods; Sensor systems; Adaptive sampling; analog electronics; analog signal processing; bio-signal recording; sensor networks; Algorithms; Artificial Intelligence; Blood Pressure Monitoring, Ambulatory; Data Interpretation, Statistical; Databases, Factual; Electrocardiography; Electrodes; Electroencephalography; Electronics; Equipment Design; Gait; Models, Statistical; Monitoring, Ambulatory; Software; Telemetry;
fLanguage :
English
Journal_Title :
Neural Systems and Rehabilitation Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1534-4320
Type :
jour
DOI :
10.1109/TNSRE.2008.2008648
Filename :
4812313
Link To Document :
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