A framework for occupancy detection and tracking using floor-vibration signals. (1st April 2022) - Drira, Slah; Smith, Ian F.C.
Highlights: A comprehensive framework for occupant detection, localization and tracking using footstep-induced floor vibrations has been successfully validated using full-scale case studies. The framework involves model-free approaches for occupant detection and a model-based approach for occupant localization and tracking. Combining information from multiple frequency components of floor vibrations improves the accuracy of event detection. Selection of appropriate frequency components for training enhances the performance of classifiers that distinguish between footstep events (single and multiple occupants walking simultaneously) and non-footstep events. Using cross-correlations between floor vibrations at several sensor locations improves the classifier performance to distinguish between the presence of either one, two, three, four, or five occupants. Model-based identification, which includes structural information and explicitly accounts for systematic errors and model bias, can accurately localize single and two occupants walking simultaneously in full-scale structures. Occupants may walk with self-selected step lengths, speed levels, and shoe types. Occupant tracking using sequential analysis and trajectory determination provides accurate and precise trajectories for up to two occupants walking simultaneously. Abstract: In sensed buildings, information related to occupant movement helps optimize important functionalities such as caregiving, energy management, and security enhancement. Typical sensing approaches for occupant tracking rely on mobile devices and cameras. These systems compromise the privacy of building occupants and may affect their behavior. Occupant detection and tracking using floor-vibration measurements that are induced by footsteps is a non-intrusive and inexpensive sensing method. Detecting the presence of occupants on a floor is challenging due to ambient noise that may mask footstep-induced floor vibrations. In addition, spurious events such as door closing and falling objects may produce vibrations that are similar to footstep impacts. These events have to be detected and disregarded. Tracking occupants is complicated due to uncertainties associated with walking styles, walking speed, shoe type, health, and mood. Also, spatial variation in structural behavior of floor slabs adds ambiguity to the task of occupant tracking, which cannot be addressed using data-driven strategies alone. In this paper, a framework for occupant detection and tracking is developed. Occupant detection is carried out based on signal information. This method outperforms existing threshold-based methods. Support-vector-machine classifiers, trained with time and frequency-domain features, successfully distinguish footsteps from spurious events and determine the number of occupants walking simultaneously. A model-based data-interpretation approach is used for occupant tracking. Structural-mechanics models are used to identify a population of possible occupant locations and trajectories. Up to two occupants can be tracked by accommodating systematic bias and uncertainties from sources such as modeling assumptions and variability in walking gaits. A hybrid framework for occupant detection and tracking that combines model-free approaches for occupancy detection with structural behavior models for tracking is developed and tested on two full-scale case studies. These studies successfully validate the utility of the framework for buildings having sparse sensor configurations that measure floor vibrations.