Event Detection Using Floor Vibrations with a Probabilistic Framework

Using floor vibrations has shown potential in human detection for security and human health applications. A key aspect of these methodologies is identifying the event location on the floor. The excitation can be due to a step, a fall, or another type of activity. Wave propagation methodologies used for this purpose face challenges due to wave dispersion, multipath fading, and unknown energy dissipation mechanisms. A new model is proposed using a Bayesian probabilistic framework to identify the location of the excitation to enable human tracking. In the proposed model, combining information from multiple sensors, the amplitude of the acceleration is a function of the distance from the event location to the sensor’s locations, and the unattenuated amplitude, the localization of the event, and the decay rate are represented by random variables. Preliminary results of the probabilistic model were obtained from a ten-impact test bed. The results showed that the model could establish the most likely area of the event location while providing a measure of the uncertainty in the estimation. However, from a probabilistic perspective, the decisions about the localization must be withheld, considering the significant uncertainty in the predicted quantities.