Dataset: Occupant Identification Using Indoor Photovoltaic Harvester Output Voltage.

Occupant identification is paramount for many building applications. Regardless, several practical concerns limit existing solutions to be ubiquitously deployed. Current systems are either intrusive, privacy-invasive, or require obtrusive, maintenance-heavy, and special-purpose infrastructure. As an alternative, the shadow pattern of a person reflected in the output voltage of a photovoltaic harvester power supply in many energy-harvesting devices can be used as a unique person identifying feature. In this paper, we present the first dataset containing the time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform occupant identification in smart homes. We collect shadow patterns of five participants from two different doors in two rooms of a building. The dataset consists of a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with associated occupant id and type of door events. Moreover, we provide insights into future research directions using the dataset. The dataset is available at https://doi.org/10.5281/zenodo.7195748