Step Detection from Power Generation Pattern in Energy-Harvesting Wearable Devices

Energy-harvesting wearable devices generate power by converting natural phenomena such as human motion into usable electricity. We conduct an experimental study to validate the feasibility of detecting steps from the power generation patterns of a wearable piezoelectric energy harvester (PEH). Four healthy adults took part in the study, which includes walking along straight and turning walkways as well as descending and ascending stairs. We find that power generation exhibits distinctive peaks for each step, making it possible to accurately detect steps using widely used peak detection algorithms. Using our PEH prototype, we successfully detected 550 steps out of 570, achieving a step detection accuracy of 96%.