Smart phones are an indispensable tool in modern day-to-day 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 thesis, we investigate the accuracy limits of one of the commonly used sensors, namely, a smart phone accelerometer. As a use case, we focus on utilizing smart phone accelerometers in structural health monitoring (SHM). Using the already deployed network of distributed citizen-owned sensors is considered a cheap alternative to standalone sensors. These devices can capture floors vibration during disasters, and consequently compute the instantaneous displacement of each floor. Hence, damage indicators defined by government standards such as peak relative displacement can be estimated. In this work, we study the displacement estimation accuracy and propose a zero-velocity update (ZUPT) method for noise cancellation. Theoretical derivation and experimental validation are presented, and we discuss the impact of sensor error on the achieved building classification accuracy. Moreover, in spite of the presence of sensor error, SHM systems can be resilient by adopting machine learning. Several algorithms such as support vector machine (SVM), K-nearest neighbor (KNN) and convolutional neural network (CNN) are adopted and compared. Techniques for addressing noise levels are proposed and the results are compared to regular noise cancellation techniques such as filtering.
Finally, since most previous work focused on modelling the sensor chip error itself, we study other sources of error such as sampling time uncertainty which is introduced by the device operating system (OS). That type of error can be considered a major contributor to the overall error, specially for sufficiently large signals. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors. The model is validated experimentally using shake table experiments, and maximum likely-hood estimation (MLE) is used to estimate the model parameters. Moreover, we derive the Cramer-Rao lower bound (CRLB) of acceleration estimation based on the proposed model.