Work

Episodic sampling: Towards energy-efficient patient monitoring with wearable sensors

Year: 2009

Type: conference-paper

Abstract: 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.

Source: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

DOI: 10.1109/iembs.2009.5333615

Authors: L.K. Au, M.A. Batalin, T. Stathopoulos, A.A.T. Bui, W.J. Kaiser

Institution: University of California, Los Angeles

Language: English

Topic: Wireless Body Area Networks

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funder: U.S. National Library of Medicine