Statistical Models For Harvested Power From Human Motion
Keywords
Ambient Energy; Empirical Measurements; Energy harvesting; Human-Motion; Statistical Models,; Wearable Devices
Abstract
This paper investigates the statistical properties of human motion-based harvested power, and provides models for the distribution, auto-correlation and cross-correlation of harvested power at different body locations, namely left wrist, right wrist, left ankle and waist. The models are developed based on empirical acceleration measurements while the subjects perform unscripted daily tasks. The measured accelerations are converted to harvestable power by assuming a velocity-damped resonant harvesting generator. The provided models enable realistic analysis and simulation of wearable communication systems with motion-based energy harvesting.
Publication Date
8-1-2015
Publication Title
IEEE Journal on Selected Areas in Communications
Volume
33
Issue
8
Number of Pages
1667-1679
Document Type
Article
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/JSAC.2015.2391871
Copyright Status
Unknown
Socpus ID
84937800897 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/84937800897
STARS Citation
Zhang, Shenqiu and Seyedi, Alireza, "Statistical Models For Harvested Power From Human Motion" (2015). Scopus Export 2015-2019. 409.
https://stars.library.ucf.edu/scopus2015/409