Smartphones are an indispensable tool in modern day-today life. Their widespread use has spawned numerous applications targeting diverse domains such as bio-medical, environment sensing and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable, since these devices are not originally designed for high accuracy sensing purposes. In this work, we investigate the accuracy limits of one of the commonly used sensors, namely, smartphone accelerometer. We focus on the efficacy of a smartphone as an acceleration measuring device, rather than focusing only on the accuracy of its internal accelerometer chip. This holistic approach includes additional errors that arise from the device operating system such as sampling time uncertainty. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors represented by a white Gaussian process. The model is validated experimentally using shake table experiments, and maximum likelyhood estimation (MLE) is used to estimate the sampling time uncertainty standard deviation. Moreover, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model.
Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an internet of things (IoT) network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to government standards. With this information, the building inventory can be classified into immediate occupancy (IO), life safety (LS) or collapse prevention (CP) categories. In this work, we propose a zero velocity update (ZUPT) technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio (IDR) distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy.
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