Energy management for wearable medical devices based on gaining–sharing knowledge algorithm

Abstract

Wearable devices are a growing field of research that can have a wide range of applications. The energy harvester is the most common source of power for wearable devices as well as in wireless sensor networks that require a battery-free operation. However, their power is restricted; consequently, power saving is crucial for wearable devices. Finding the best schedule for the various tasks that run on the wearable device can help to reduce power consumption. This paper presents a task scheduler for wearable medical devices based on Gaining–Sharing Knowledge (GSK) algorithm. The purpose of this task scheduler is to handle the tasks of a heart rate sensor and a temperature sensor to optimize the energy consumption throughout wearable medical devices. The proposed GSK-based scheduling algorithm is assessed against the state-of-the-art technique. The data used in our experiments are collected from an in-lab prototype.

Early detection of hypo/hyperglycemia using a microneedle electrode array-based biosensor for glucose ultrasensitive monitoring in interstitial fluid

“…The wearable device is powered by a 3 V lithium battery with a current capacity of 220 mA h. A smart scheduling system is implemented to optimize the power consumption. 60,61 Hence, the average current consumption of the device is 300 μA which means that the device can last for 22 days in operation. The system is designed to read the glucose level every 5 minutes and send the readings via BLE5 to the mobile application every hour.…”