Episodic Sampling: Towards Energy-efficient Patient Monitoring with Wearable Sensors

被引：17

作者：

Au, Lawrence K. ^{[1
]}

Batalin, Maxim A. ^{[1
]}

Stathopoulos, Thanos ^{[1
]}

Bui, Alex A. T. ^{[2
]}

Kaiser, William J. ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Dept Elect Engn, Los Angeles, CA 90095 USA

[2] Univ Calif Los Angeles, Dept Radiol Sci, Los Angeles, CA 90095 USA

来源：

2009 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-20 | 2009年

基金：

美国国家卫生研究院;

关键词：

QUALITY-OF-LIFE;

D O I：

10.1109/IEMBS.2009.5333615

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Energy efficiency presents a critical design challenge in wireless, wearable sensor technology, mainly because of the associated diagnostic objectives required in each monitoring application. In order to maximize the operating lifetime during real-life monitoring and maintain sufficient classification accuracy, the wearable sensors require hardware support that allows dynamic power control on the sensors and wireless interfaces as well as monitoring algorithms to control these components intelligently. This paper introduces a context-aware sensing technique known as episodic sampling a method of performing context classification only at specific time instances. Based on Additive-Increase/Multiplicative-Decrease (AIMD), episodic sampling demonstrates an energy reduction of 85 percent with a loss of only 5 percent in classification accuracy in our experiment.

引用

页码：6901 / +

页数：2

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