Vibration-based occupant detection using a multiple-model approach

Sensor-based occupant detection has the potential to make an important contribution to the development of structures of the future. Applications that may benefit from robust occupant detection include patient detection in hospitals, senior citizen housing facilities, personnel localization in emergencies as well as user behavior studies. In this contribution, an occupant detection and localization methodology based on recorded vibration time-series is outlined. The movement of an occupant on a floor generates vibrations that can be recorded by accelerometers. However, measured vibrations contain measurement noise and are contaminated by ambient sources of vibration such as machinery and nearby traffic. This contribution relies on using filtered vibration time-series to detect events of moving occupants and subsequently perform model-based localization of occupants using error-domain model falsification. The error-domain model-falsification methodology utilizes multiple models to deal with ambiguity related to the inverse problem of occupant localization. By explicitly incorporating uncertainty from various sources using engineering heuristics, error-domain model falsification provides a set of candidate locations based on measurements obtained through a coarse sensor configuration. The results from this methodology provide in a binary manner the presence or absence of an occupant and subsequently candidate locations of the occupant on the floor of a 200 m2 hall that is equipped with only four accelerometers.