Automatic health monitoring helps enable independent living for the elderly by providing specific information to caregivers. This goal, called aging in place,is increasingly important as an unprecedented portion of the population enters old age. I introduce the simultaneous tracking and activity recognition (STAR) problem,whose solution provides this key information. I propose using data from many minimally invasive sensors commonly found in home security systems to provide simultaneous room-level tracking and recognition of many of the activities of daily living (ADLs). ADLs have been chosen by physicians to gauge the severity of cognitive and physical ailments. I describe a Rao-Blackwellised particle filter for room level tracking, rudimentary activity recognition, and data association, as well as a Monte Carlo EM approach for online parameter learning. I demonstrate results from experiments in an instrumented home and on simulated data. Proposed extensions improve the approach and add more complex activity recognition. We discuss how to integrate a growing vocabulary of activities into the tracker.

Model-based clustering techniques have been widely used and have shown promising results in many applications involving complex data. This paper presents a unified framework for probabilistic model-based clustering based on a bipartite graph view of data and models that highlights the commonalities and differences among existing model-based clustering algorithms. In this view, clusters are represented as probabilistic models in a model space that is conceptually separate from the data space. For partitional clustering, the view is conceptually similar to the ExpectationMaximization (EM) algorithm. For hierarchical clustering, the graph-based view helps to visualize critical/important distinctions between similarity-based approaches and model-based approaches.

Abstract not found

We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumbersome) manual categorization. We provide an EM algorithm for training a mixture of HMMs and show that additional static user data can be incorporated easily to possibly enhance the labelling of users. Furthermore, we use prior knowledge to enhance generalization and avoid numerical problems. We use parameter tying to decrease the danger of over tting and to reduce computational overhead. We put a at prior on the parameters to deal with the problem that certain transitions between page categories occur very seldom or not at all, in order to ensure that a nonzero transition probability between these categories nonetheless remains. In applications to arti cial data and real-world web logs we demonstrate the usefulness of our approach. We train a mixture of HMMs on arti cial navigation patterns, and show that the correct model is being learned. Moreover, we show that the use of static 'satellite data' may enhance the labeling of shorter navigation patterns. When applying a mixture of HMMs to realworld web logs from a large Dutch commercial web site, we demonstrate that sensible page categorizations are being learned.

In order for robotic systems to be successful in domains with other agents possibly interfering with the accomplishing of goals, the agents must be able to adapt to the opponents' behavior. The more quickly the agents can respond to a new situation, the better they will perform. We present an approach to doing adaptation which relies on classification of the current adversary into predefined adversary classes. For feature extraction, we present a windowing technique to abstract useful but not overly complicated features. In order to take into account the spatial locality of topological differences, we use a previously developed similarity metric. The feature extraction and classification steps are fully implemented in the domain of simulated robotic soccer, and experimental results are presented.

Clustering problems are central to many knowledge discovery and data mining tasks. However, most existing clustering methods can only work with fixed-dimensional representations of data patterns. In this paper, we study the clustering of data patterns that are represented as sequences or time series possibly of di#erent lengths. We propose a model-based approach to this problem using mixtures of autoregressive moving average (ARMA) models. We derive an expectation-maximization (EM) algorithm for learning the mixing coe#cients as well as the parameters of the component models. The algorithm can determine the number of clusters in the data automatically. Experiments were conducted on a number of simulated and real datasets. Results from the experiments show that our method compares favorably with another method recently proposed by others for similar time series clustering problems.

Abstract not found

Clustering time series is an active research area with applications in many fields. One common feature of time series is the likely presence of outliers. These uncharacteristic data can significantly e#ect the quality of clusters formed. This paper evaluates a method of overcoming the detrimental e#ects of outliers. We describe some of the alternative approaches to clustering time series, then specify a particular class of model for experimentation with k-means clustering and a correlation based distance metric. For data derived from this class of model we demonstrate that discretising the data into a binary series of above and below the median improves the clustering when the data has outliers.

Agents need to know the effects of their actions. Strong associations between actions and effects can be found by counting how often they co-occur. We present an algorithm that learns temporal patterns expressed as fluents, propositions with temporal extent. The fluent-learning algorithm is hierarchical and unsupervised. It works by maintaining co-occurrence statistics on pairs of fluents. In experiments on a mobile robot, the fluent-learning algorithm found temporal associations that correspond to effects of the robot's actions.

Motivated by recent experimental developments in functional genomics, we construct and test a numerical technique for inferring process pathways, in which one process calls another process, from time series data. We validate using a case in which data are readily available and formulate an extension, appropriate for genetic regulatory networks, which exploits Bayesian inference and in which the present–day undersampling is compensated for by prior understanding of genetic regulation. Preprint number: NSF-ITP-02-47 1