This paper introduces a hierarchical Markov model that can learn and infer a user’s daily movements through the 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 mode of transportation or her goal. We apply Rao-Blackwellised particle filters for efficient inference both at the low level and at the higher levels of the hierarchy. Significant locations such as goals or locations where the user frequently changes mode of transportation are learned from GPS data logs without requiring any manual labeling. We show how to detect abnormal behaviors (e.g. taking a wrong bus) by concurrently tracking his activities with a trained and a prior model. Experiments show that our model is able to accurately predict the goals of a person and to recognize situations in which the user performs unknown activities.

Abstract. Location awareness is an important capability for mobile computing. Yet inexpensive, pervasive positioning—a requirement for wide-scale adoption of location-aware computing—has been elusive. We demonstrate a radio beacon-based approach to location, called Place Lab, that can overcome the lack of ubiquity and high-cost found in existing location sensing approaches. Using Place Lab, commodity laptops, PDAs and cell phones estimate their position by listening for the cell IDs of fixed radio beacons, such as wireless access points, and referencing the beacons ’ positions in a cached database. We present experimental results showing that 802.11 and GSM beacons are sufficiently pervasive in the greater Seattle area to achieve 20-40 meter median

Location systems have long been identified as an important component of emerging mobile applications. Most research on location systems has focused on precise location in indoor environments. However, many location applications (for example, location-aware web search) become interesting only when the underlying location system is available ubiquitously and is not limited to a single office environment. Unfortunately, the installation and calibration overhead involved for most of the existing research systems is too prohibitive to imagine deploying them across, say, an entire city. In this work, we evaluate the feasibility of building a wide-area 802.11 Wi-Fi-based positioning system. We compare a suite of wireless-radio-based positioning algorithms to understand how they can be adapted for such ubiquitous deployment with minimal calibration. In particular, we study the impact of this limited calibration on the accuracy of the positioning algorithms. Our experiments show that we can estimate a user’s position with a median positioning error of 13–40 meters (depending upon the characteristics of the environment). Although this accuracy is lower than existing positioning systems, it requires substantially lower calibration overhead and provides easy deployment and coverage across large metropolitan areas. 1

this paper, we describe an algorithm for extracting significant places from a trace of coordinates. Furthermore, we experimentally evaluate the algorithm with real, long-term data collected from three participants using a Place Lab client [15], a software client that computes location coordinates by listening for RF-emissions from known radio beacons in the environment (e.g. 802.11 access points, GSM cell towers)

Context is a critical ingredient of ubiquitous computing. While it is possible to use specialized sensors and beacons to measure certain aspects of a user’s context, we are interested in what we can infer from using the existing 802.11 wireless network infrastructure that already exists in many places. The context parameters we infer are the location of a client (with a median error of 1.5 meters) and an indicator of whether or not the client is in motion (with a classification accuracy of 87%). Our system, called LOCADIO, uses Wi-Fi signal strengths from existing access points measured on the client to infer both pieces of context. For motion, we measure the variance of the signal strength of the strongest access point as input to a simple two-state hidden Markov model (HMM) for smoothing transitions between the inferred states of “still ” and “moving. ” For location, we exploit the fact that Wi-Fi signal strengths vary with location, and we use another HMM on a graph of location nodes whose transition probabilities are a function of the building’s floor plan, expected pedestrian speeds, and our still/moving inference. Our probabilistic approach to inferring context gives a convenient way of balancing noisy measured data such as signal strengths against our a priori assumptions about a user’s behavior. 1.

Abstract. 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 software. The system uses a novel inference engine that does not require users to explicitly provide information about the start or ending points of their journeys; instead this information is learned from users ’ past behavior. Futhermore, we demonstrate how route errors can be detected and how the system helps to correct the errors with real-time transit information. In addition we present a novel solution to the problem of labeling positions with place names. 1

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.

In this work we identify the structure inherent in daily human behavior with models that can accurately analyze, predict and cluster multimodal data from individuals and groups. We represent this structure by the principal components of the complete behavioral dataset, a set of characteristic vectors we have termed eigenbehaviors. In our model, an individual’s behavior over a specific day can be approximated by a weighted sum of his or her primary eigenbehaviors. When these weights are calculated halfway through a day, they can be used to predict the day’s remaining behaviors with a 79 % accuracy for our test subjects. Additionally, we show that users of a similar demographic can be clustered into a “behavior space ” spanned by a set of their aggregate eigenbehaviors. These behavior spaces make it possible to determine the behavioral similarity between both individuals and groups, enabling 96 % classification accuracy of group affiliations. This approach capitalizes on the large amount of rich data previously captured during the Reality Mining study from mobile phones continuously logging location, proximate people, and communication of 100 subjects at MIT over the course of nine months.

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 high-level context into account. Our system uses relational Markov networks to represent the hierarchical activity model that encodes the complex relations among GPS readings, activities and significant places. We apply FFT-based message passing to perform efficient summation over large numbers of nodes in the networks. We present experiments that show significant improvements over existing techniques. 1

In this paper, we address the problem of extending a relational database system to facilitate efficient real-time application of dynamic probabilistic models to streaming data. We use the recently proposed abstraction of model-based views for this purpose, by allowing users to declaratively specify the model to be applied, and by presenting the output of the models to the user as a probabilistic database view. We support declarative querying over such views using an extended version of SQL that allows for querying probabilistic data. Underneath we use particle filters, a class of sequential Monte Carlo algorithms commonly used to implement dynamic probabilistic models, to represent the present and historical states of the model as sets of weighted samples (particles) that are kept up-to-date as new readings arrive. We develop novel techniques to convert the queries on the model-based view directly into queries over particle tables, enabling highly efficient query processing. Finally, we present experimental evaluation of our prototype implementation over sensor data from the Intel Lab dataset that demonstrates the feasibility of online modeling of streaming data using our system and establishes the advantages of such tight integration between dynamic probabilistic models and database systems. 1