Kalantarian Haik, Sideris Costas, Mortazavi Bobak, Alshurafa Nabil, Sarrafzadeh Majid
IEEE Trans Biomed Eng. 2017 Mar;64(3):621-628. doi: 10.1109/TBME.2016.2570210. Epub 2016 May 18.
The objective of this paper is to describe and evaluate an algorithm to reduce power usage and increase battery lifetime for wearable health-monitoring devices.
We describe a novel dynamic computation offloading scheme for real-time wearable health monitoring devices that adjusts the partitioning of data processing between the wearable device and mobile application as a function of desired classification accuracy.
By making the correct offloading decision based on current system parameters, we show that we are able to reduce system power by as much as 20%.
We demonstrate that computation offloading can be applied to real-time monitoring systems, and yields significant power savings.
Making correct offloading decisions for health monitoring devices can extend battery life and improve adherence.
本文的目的是描述和评估一种算法，以降低可穿戴健康监测设备的功耗并延长电池续航时间。
我们描述了一种用于实时可穿戴健康监测设备的新型动态计算卸载方案，该方案根据所需的分类精度调整可穿戴设备与移动应用程序之间的数据处理分配。
通过根据当前系统参数做出正确的卸载决策，我们表明能够将系统功耗降低多达20%。
我们证明了计算卸载可应用于实时监测系统，并能显著节省功耗。
为健康监测设备做出正确的卸载决策可以延长电池寿命并提高依从性。
