We introduce a system for sensing complex social systems with data collected from one hundred mobile phones over the course of six months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.

Wearable computers have the potential to act as intelligent agents in everyday life and assist the user in a variety of tasks, using context to determine how to act. Location is the most common form of context used by these agents to determine the user's task. However, another potential use of location context is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single--user and collaborative scenarios. 1

Wearable computers have the potential to act as intelligent agents in everyday life and assist the user in a variety of tasks depending on the context. Location is the most common form of context used by these agents to determine the user's task. However, another potential use is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single--user and collaborative scenarios.

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.

The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months have generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify selfreport

Governments and commercial institutions have conducted detailed time-use studies for several decades. In these studies, participants give a detailed record of their activities, locations, and other data over a day, week, or longer period. These studies are particularly valuable for the ubicomp community because of the large number of participants (often the tens of thousands), and because of their public availability. In this paper, we show how to use the data from these studies to provide validated and cheap (although noisy) classifiers, baseline metrics, and other benefits for activity inference applications. Author Keywords Time-use studies, diary studies, activity inference, evaluation methodologies, ubiquitous computing, mobile computing. ACM Classification Keywords H.1.m. Models and Principles: Miscellaneous.

The transferal of the desktop interface to the world at large is not the goal of ubiquitous computing. Rather, ubiquitous computing strives to increase the responsiveness of the world at large to the individual. A large part of this responsiveness is improved communication with other individuals. In this paper we describe a system that can enable ad--hoc collaboration between several people by creating a model of the daily schedules of individuals and by performing predictions based on this model. Using GPS data we learn to distinguish locations and track the times that these locations are visited. In addition, we use Markov models to predict which locations might be visited next based on the user's previous behavior.

An information and communication technology infrastructure is rapidly emerging that enables the delivery of location-based services to vast numbers of mobile users. Services will benefit from being aware of not only the user’s location, but also the user’s current destination and route towards the destination. This paper describes a component that enables the use of geocontext. Using GPS data, the component gathers a driver’s routes and associates them with usage meta-data. Other services may then provide the component with a driver ID, the time of the day, and a location, in return obtaining the likely routes for the driver.

Understanding the regularity and the variability of individual travel behaviour over time has been one of the key issues in travel behaviour research for three decades. A deeper insight into the long-term mobility patterns of persons and households has been so far restricted by the limited availability of longitudinal data, though.

This paper presents an interactive analysis and visualization framework for behavior histories, called mPATH framework. In ubiquitous computing environment, it is possible to infer human activities through various sensors and accumulation of their data. Visualization of such human activities is one of the key issues in terms of memory and sharing our experiences, since it acts as a memory assist when we recall, talk about, and report what we did in the past. However, current approaches for analysis and visualization are designed for a specific use, and therefore can not be applied to diverse use. Our approach provides users with programmability by a visual language interface for analyzing and visualizing the behavior histories. The framework includes icons representing data sources of behavior histories, analysis filters, and viewers. By composing them, users can create their own analysis method of behavior histories. We also demonstrate several visualizations on the framework. The visualizations show the flexibility of creating behavior history viewers on the mPATH framework.