间歇采样：利用可穿戴传感器实现节能型患者监测
Episodic sampling: towards energy-efficient patient monitoring with wearable sensors.
作者信息
Au Lawrence K, Batalin Maxim A, Stathopoulos Thanos, Bui Alex A T, Kaiser William J
机构信息
Department of Electrical Engineering, University of California, Los Angeles, CA 90095, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:6901-5. doi: 10.1109/IEMBS.2009.5333615.
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.
能源效率在无线可穿戴传感器技术中是一项关键的设计挑战，主要是因为每个监测应用中都有相关的诊断目标。为了在实际监测期间最大化运行寿命并保持足够的分类精度，可穿戴传感器需要硬件支持，以便对传感器和无线接口进行动态功率控制，同时还需要监测算法来智能地控制这些组件。本文介绍了一种名为情景采样的上下文感知传感技术——一种仅在特定时间实例执行上下文分类的方法。基于加性增加/乘性减少（AIMD），情景采样在我们的实验中显示出能源减少了85%，而分类精度仅损失了5%。
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