The Taub Faculty of Computer Science Events and Talks

The proliferation of modern mobile phones has opened up unprecedented opportunities for leveraging location-tracking capabilities to extract individual mobility patterns and contextual information. However, existing approaches heavily rely on analyzing geolocation data obtained from Global Navigation Satellite System (GNSS) observations, which are limited when used in enclosed spaces. This talk presents new algorithms for efficient mobility data analysis and contextual learning by utilizing the WiFi infrastructure that exists today in most indoor environments. Firstly, I will introduce SWATSON, an unsupervised trajectory segmentation algorithm that is designed to partition a continuous sequence of data points into homogeneous segments. SWATSON effectively utilizes temporal constraints, mitigates noise, and exhibits applicability across various domains. By employing SWATSON, I will demonstrate an approach to analyzing movement in indoor environments without needing a localization process. Consequently, the integration of SWATSON will be explored to create personalized semantic categorical place labeling, such as assigning the label "work" to specific locations. I will present a supervised learning-based classification model, leveraging WiFi-based attributes for sequence-based modeling and spatio-temporal feature generation to enhance the accuracy of semantic place labeling. Experiments show a 97% V-measure score in segmenting data point into distinct spaces, notably surpassing the performance of GNSS-based solutions in semantic place labeling, thereby highlighting substantial progress in the fields of trajectory analysis and predictive modeling.