Occupant-detection strategy using footstep-induced floor vibrations

Identification of occupant presence and location inside buildings is essential to functional goals such as security, healthcare, and energy management. Floor-vibration measurements, induced by footstep impacts, provide a non-intrusive sensing method for occupant identification, unlike cameras and smartphones. Detecting the presence of an occupant is a necessary first step for occupant location identification. A challenge for occupant detection is ambient noise that may hide footstep-induced floor-vibration signatures. Also, spurious events such as door closing, chair dragging and falling objects may result in vibrations that have similarities with footstep-impact events. In this paper, an accurate occupant-detection strategy for structures with varying rigidity is outlined. Event detection is based on computing the standard deviation of a moving window over measurements at various frequency ranges. Using a classification method, footsteps are distinguished from other events. This strategy enhances detection of footstep-impact events compared with methods that employ only thresholds, thereby reducing false positives (incorrect detection) and false negatives (undetected events). Footstep-impact events may then be used for footstep impact localization using model-based approaches. Finally, the utility of this strategy for footstep-event detection is evaluated using a full-scale case study.