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This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through an urban community. The model uses multiple levels of abstraction in order to bridge the gap between raw GPS sensor measurements and high level information such as a user’s destination and mode of transportation. To achieve efficient inference, we(More)
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. This paper describes how to extract a person’s activities and significant places from traces of GPS data. The system uses hierarchically structured conditional random fields to generate a consistent model of a person’s activities and places. In(More)
L ocation awareness is important to many pervasive computing applications. Unfortunately, no location sensor takes perfect measurements or works well in all situations. Thus, the motivation behind this article is twofold. First, we believe the pervasive computing community will benefit from a concise survey of Bayesian-filter techniques. Because no sensor(More)
We present a method of learning a Bayesian model of a traveler moving through an urban environment. This technique is novel in that it simultaneously learns a unified model of the traveler’s current mode of transportation as well as his most likely route, in an unsupervised manner. The model is implemented using particle filters and learned using(More)
In this paper we define a general framework for activity recognition by building upon and extending Relational Markov Networks. Using the example of activity recognition from location data, we show that our model can represent a variety of features including temporal information such as time of day, spatial information extracted from geographic databases,(More)
We present an automated transportation routing system, called “Opportunity Knocks,” whose goal is to improve the efficiency, safety and independence of individuals with mild cognitive disabilities. Our system is implemented on a combination of a Bluetooth sensor beacon that broadcasts GPS data, a GPRS-enabled cell-phone, and remote activity inference(More)
While conditional random fields (CRFs) have been applied successfully in a variety of domains, their training remains a challenging task. In this paper, we introduce a novel training method for CRFs, called virtual evidence boosting, which simultaneously performs feature selection and parameter estimation. To achieve this, we extend standard boosting to(More)
Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. We show how to extract and label a person’s activities and significant places from traces of GPS data. In contrast to existing techniques, our approach simultaneously detects and classifies the significant locations of a person and takes the(More)
Tracking the activity of people in indoor environments has gained considerable attention in the robotics community over the last years. Most of the existing approaches are based on sensors which allow to accurately determine the locations of people but do not provide means to distinguish between different persons. In this paper we propose a novel approach(More)
We present research on developing models that forecast traffic flow and congestion in the Greater Seattle area. The research has led to the deployment of a service named JamBayes, that is being actively used by over 2,500 users via smartphones and desktop versions of the system. We review the modeling effort and describe experiments probing the predictive(More)