Several studies have focused on tracking inhabitants in a smart environment. However, these approaches have often required inhabitants to wear devices, or they have needed lengthy pre-calibration before tracking could be engaged. Such approaches are often intrusive, thus making inhabitants uncomfortable when the ultimate purpose is to provide convenient services. Thus, the approaches are somewhat controversial. In this study, we constructed an environment consisting of load sensors, with wooden flooring covering the surfaces as in a normal home environment. The wooden flooring provided a flat surface for inhabitants to walk on but caused clutter in the load sensor. Thus, we applied Probabilistic Data Association and LeZi-Update to analyze the cluttered pressure phenomenon collected by the load sensors and to determine the inhabitants' locations and track their movements. With our non-intrusive approach, there is no need for inhabitants to wear any devices, and there are no complicated pre-settings, unlike other approaches.
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