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Detecting Door Events Using a Smartphone via Active Sound Sensing

Event detection of indoor objects, including doors, has a wide variety of applications, including intruder detection, HVAC control, and surveillance of independently living elderly people. Hence, this has been the focus of multiple research projects in the UbiComp research community. Herein, we propose a method to accurately detect door events in an indoor environment, without the installation and maintenance costs of using distributed ubiquitous sensors. In particular, we recognize the events of multiple doors existing in the environment via active sound probing using a disused smartphone installed in the environment. We perform event recognition by fusing the analysis of the Doppler shift caused by the moving doors with the acoustic characteristics describing the open/close states of the doors acquired via impulse response. To accurately distinguish between the events of different doors via sound probing, our method employs the time-series analysis of the Doppler shift as well as the active sound probing using directional high-frequency sine waves and stereo sound recording. In addition, by incorporating prior knowledge about the state transitions of a door object into a recognition model, we attempt to improve the accuracy of event recognition. Moreover, our method is capable of recognizing walking activities of a person related to door events in the environment, which are necessary information for applications such as HVAC control that require information about both door events and human presence.