Maximizing output power of a CFPG micro energy-harvester for wearable medical sensors

Energy Harvesting refers to the process of capturing and storing energy from the ambient environment. Kinetic energy harvested from the human body motion seems to be one of the most convenient and attractive solutions for wearable wireless sensors in healthcare applications. Due to their small size, such sensors are often powered by small batteries which might necessitate frequent recharge or even sensor replacement. Energy harvesting can prolong the battery lifetime of these sensors. This could directly impact their everyday use and significantly help their commercial applications such as remote monitoring. In this paper, our aim is to develop a Simulink model of the CFPG device that can be used to study temporal behavior of the generated power. Having such a dynamic model, not only helps to have a more accurate estimation of the amount of power generated from various human movements, but also allows us to further optimize the design parameters of the micro-harvester (e.g. size/dimension, electrostatic holding force, etc.) with the characteristics of the input acceleration (i.e. human activity).