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

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.

Fig. 1

Wearable Device and Sensors

Fig. 2

Episodic Sampling vs. Continuous Sampling

Fig. 3

Raw Waveforms from Respiratory Sensor

Fig. 4

Classification with Episodic Sampling

Fig. 5

Experimental Results - Continuous vs. Episodic Sampling