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|Author(s):||Nathalie Yarkony; Kamran Sayrafian; Antonio M. Possolo;|
|Title:||Statistical Modeling of Harvestable Kinetic Energy for Wearable Medical Sensors|
|Published:||June 15, 2010|
|Abstract:||Energy Harvesting (EH) refers to the process of capturing and storing energy from external sources or ambient environment. Kinetic energy harvested from the human body motion seems to be one of the most convenient and attractive solution for wearable wireless sensors in health care applications. Due to their small size, such sensors have a very limited battery power supply, which necessitates 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 estimate the amount of harvestable energy from typical human motion. To simplify the measurement process, we focus on the amount of kinetic energy harvested from the human forearm motion. We provide statistical analysis of measurements taken from 40 test subjects over a period of 8 hours during the day. Using this information and knowing the operational architecture of the harvesting device, the distribution of harvestable energy can also be determined. Our objective is to study whether kinetic energy generated by typical human forearm motion could be a promising supplemental energy resource that prolongs the operational lifetime of wearable medical sensors.|
|Proceedings:||11th Annual IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks|
|Dates:||June 14-29, 2010|
|Keywords:||Energy Harvesting, Wearable wireless sensors, Uncertainty analysis, Triaxial accelerometer|
|Research Areas:||Health IT|
|PDF version:||Click here to retrieve PDF version of paper (301KB)|