An adaptive sampling system for sensor nodes in body area networks.
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.
Electrical Engineering Department, National Sun Yat-Sen University, 804 Kaohsiung, Taiwan. rrieger@mail.nsysu.edu.tw
Robert
R
Rieger
John T
JT
Taylor
D000465:Algorithms; D001185:Artificial Intelligence; D018660:Blood Pressure Monitoring, Ambulatory; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D004562:Electrocardiography; D004566:Electrodes; D004569:Electroencephalography; D004581:Electronics; D004867:Equipment Design; D005684:Gait / Q000502:physiology; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000295:instrumentation*; D012984:Software; D013686:Telemetry