Anomaly Detection and probable path prediction for Single and Multiperson-Application in Smart Homes

The most popular outdoor positioning system, global positioning system (GPS), does not perform well in indoor environment. Because this system primarily depends on the signal propagation in the air and complex architecture of buildings will interfere with signal propagation, i.e., its indoor positioning performance will be limited by the line-of-sight nature. While the drawbacks of GPS, other indoor positioning techniques (such as Wi-Fi based, RFID based) can provide Location-based-service (LBS) for various applications, which make our life comfortable and smart. As one kind of these sensing and positioning techniques, the passive Electric Field Sensing has numerous advantages compared to the others, e.g., lower power consumption and no personal information and specific positioning tokens required. So it is applied in our Smart Floor system to position and track movement for users, which mainly aims at the elderly care. On the other hand, a passive EFS-based positioning system might be susceptible to disturbance, due to the aliasing effect and noises from environment. To address this problem, I studied and investigated the Anomaly Detection issue in the Machine Learning domain, which aims at discovering proper ML algorithms to improve the positioning and movement prediction performance of our Smart Floor system. In this thesis, I proposed a novel ML algorithm for this goal, namely the Dictionary-based Anomaly Detection Algorithm. Compared with other existing algorithms, this dictionary algorithm exploits not only the normal data but also coupling outliers to obtain our desired results, i.e. indoor positions of users. Furthermore, combining with a customized positioning scheme relying on anchor points, the Dictionary-based indoor Positioning and Movement Prediction approach preformed well in our living laboratory. Moreover as discussion and expansion, the Dictionary-based Anomaly Detection Algorithm is especially practicable in application scenarios where a large amount of outliers and normal data are always at the same time observed.