Event Detection and Localization Using Machine Learning on a Staircase
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abstract
Recent years have seen a push towards smart buildings that are energy efficient and proactive in decision making by detecting building events. Instrumentation of structures with sensors such as accelerometers or thermocouples is an essential element for providing the building with the necessary capabilities to enhance the occupants comfort, safety and overall quality-of-life. As a result, vast amounts of data are collected that if correctly parsed can produce meaningful information for this purpose. One of the much-needed information about a buildings activities is event localization. In many situations, event localization, based on traditional wave propagation techniques associated with vibrations, is a challenging task in an active environment as there is little control over the noise concurrent with the event. Determining ways to process sensor data efficiently and effectively will make user interaction with the building more intuitive and enhance user experience.
