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A wearable based long-term lifelogging system is desirable for the purpose ofreviewing and improving users' lifestyle habits. Energy harvesting (EH) is apromising means for realizing sustainable lifelogging. However, present EHtechnologies suffer from instability of the generated electricity caused bychanges of environment, e.g., the output of a solar cell varies based on itsmaterial, light intensity, and light wavelength. In this paper, we leveragethis instability of EH technologies for other purposes, in addition to its useas an energy source. Specifically, we propose to determine the variation ofgenerated electricity as a sensor for recognizing "places" where the uservisits, which is important information in the lifelogging system. First, weinvestigate the amount of generated electricity of selected energy harvestingelements in various environments. Second, we design a system called EHAAS(Energy Harvesters As A Sensor) where energy harvesting elements are used as asensor. With EHAAS, we propose a place recognition method based onmachine-learning and implement a prototype wearable system. Our prototypeevaluation confirms that EHAAS achieves a place recognition accuracy of 88.5%F-value for nine different indoor and outdoor places. This result is betterthan the results of existing sensors (3-axis accelerometer and brightness). Wealso clarify that only two types of solar cells are required for recognizing aplace with 86.2% accuracy.

EHAAS: Energy Harvesters As A Sensor for Place Recognition on Wearables