Towards human-powered IoT: Optimizing harvested power from human daily motion

Human kinetic energy is considered to be a promising green energy source to enable human-powered Internet of Things (IoT), as constrained lifetime has become a bottleneck problem for IoT devices. However, the scarce energy collected by human motion severely restricts the operation of human-powered IoT and stresses the need for an optimized inertial harvester to provide more energy from human daily activities. In this paper, we investigate the feasibility and efficiency of using a single frequency inertial energy harvester, which is optimized based on a typical one-day motion of a human subject, to harvest kinetic energy from multiple-day activities of the same human subject. To facilitate this investigation, we propose a novel optimization framework to maximize the harvested power from human daily motion using a single-frequency energy harvester. By analyzing the frequency characteristics of human daily motion and the inertial harvester model, the optimal inertial harvester parameters are determined to maximize power generation from a typical one-day motion, and are used to harvest power from the same human subject's motion of other days. The real world human motion dataset is used for evaluation. The results demonstrate that the propose method can maximize power generated from one-day motion. Furthermore, the optimal harvester parameters determined by one-day trace can also achieve near-optimal harvested power from other days.