Catálogo de publicaciones - libros

This paper presents a novel ubiquitous computing device, the SenseCam, a sensor augmented wearable stills camera. SenseCam is designed to capture a digital record of the wearer’s day, by recording a series of images and capturing a log of sensor data. We believe that reviewing this information will help the wearer recollect aspects of earlier experiences that have subsequently been forgotten, and thereby form a powerful retrospective memory aid. In this paper we review existing work on memory aids and conclude that there is scope for an improved device. We then report on the design of SenseCam in some detail for the first time. We explain the details of a first in-depth user study of this device, a 12-month clinical trial with a patient suffering from amnesia. The results of this initial evaluation are extremely promising; periodic review of images of events recorded by SenseCam results in significant recall of those events by the patient, which was previously impossible. We end the paper with a discussion of future work, including the application of SenseCam to a wider audience, such as those with neurodegenerative conditions such as Alzheimer’s disease.

Drawing on Value Sensitive Design, we developed a workable privacy addendum for an open source software license that not only covers intellectual property rights while allowing software developers to modify the software (the usual scope of an open source license), but also addresses end-user privacy. One central innovation of our work entails the integration of an informed consent model and a threat model for developing privacy protections for ubiquitous location aware systems. We utilized technology that provided a device’s location information in real-time: Intel’s POLS, a “sister” system to Intel’s Place Lab. In January 2006, POLS was released under a license combining the substantive terms of the Eclipse Public License together with this privacy addendum. In this paper, we describe how we developed the privacy addendum, present legal terms, and discuss characteristics of our design methods and results that have implications for protecting privacy in ubiquitous information systems released in open source.

Recognition of everyday physical activities is difficult due to the challenges of building informative, yet unobtrusive sensors. The most widely deployed and used mobile computing device today is the mobile phone, which presents an obvious candidate for recognizing activities. This paper explores how coarse-grained GSM data from mobile phones can be used to recognize high-level properties of user mobility, and daily step count. We demonstrate that even without knowledge of observed cell tower locations, we can recognize mobility modes that are useful for several application domains. Our mobility detection system was evaluated with GSM traces from the everyday lives of three data collectors over a period of one month, yielding an overall average accuracy of 85%, and a daily step count number that reasonably approximates the numbers determined by several commercial pedometers.

This paper examines the positioning accuracy of a GSM beacon-based location system in a metropolitan environment. We explore five factors effecting positioning accuracy: location algorithm choice, scan set size, simultaneous use of cells from different providers, training and testing on different devices, and calibration data density. We collected a 208-hour, 4350Km driving trace of three different GSM networks covering the Seattle metropolitan area. We show a median error of 94m in downtown and 196m in residential areas using a single GSM network and the best algorithm for each area. Estimating location using multiple providers’ cells reduces median error to 65-134 meters and 95% error to 163m in the downtown area, which meets the accuracy requirements for E911. We also show that a small 60-hour calibration drive is sufficient for enabling a metropolitan area similar to Seattle.

We describe a method called that uses a history of a driver’s destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips.

A sedentary lifestyle is a contributing factor to chronic diseases, and it is often correlated with obesity. To promote an increase in physical activity, we created a social computer game, Fish’n’Steps, which links a player’s daily foot step count to the growth and activity of an animated virtual character, a fish in a fish tank. As further encouragement, some of the players’ fish tanks included other players’ fish, thereby creating an environment of both cooperation and competition. In a fourteen-week study with nineteen participants, the game served as a catalyst for promoting exercise and for improving game players’ attitudes towards physical activity. Furthermore, although most player’s enthusiasm in the game decreased after the game’s first two weeks, analyzing the results using Prochaska’s Transtheoretical Model of Behavioral Change suggests that individuals had, by that time, established new routines that led to healthier patterns of physical activity in their daily lives. Lessons learned from this study underscore the value of such games to encourage rather than provide negative reinforcement, especially when individuals are not meeting their own expectations, to foster long-term behavioral change.

Hitchers is a game for mobile phones that exploits cellular positioning to support location-based play. Players create digital hitch hikers, giving them names, destinations and questions to ask other players, and then drop them into their current phone cell. Players then search their current cell for hitchers, pick them up, answer their questions, carry them to new locations and drop them again, providing location-labels as hint to where they can be found. In this way, hitchers pass from player to player, phone to phone and cell to cell, gathering information and encouraging players to label cells with meaningful place names. A formative study of Hitchers played by 47 players over 4 months shows how the seams in cellular positioning, including varying cell size, density and overlap, affected the experience. Building on previous discussions of designing for uncertainty and seamful design, we consider five ways of dealing with these seams: removing, hiding, managing, revealing and exploiting them. This leads us to propose the mechanism of a dynamic search focus, to explore new visualization tools for cellular data, and to reconsider the general relationship between ‘virtual’ and ‘physical’ worlds in location-based games.

Ubiquitous computing technologies create new opportunities for preventive healthcare researchers to deploy behavior modification strategies outside of clinical settings. In this paper, we describe how strategies for motivating behavior change might be embedded within usage patterns of a typical electronic device. This interaction model differs substantially from prior approaches to behavioral modification such as CD-ROMs: sensor-enabled technology can drive interventions that are timelier, tailored, subtle, and even fun. To explore these ideas, we developed a prototype system named On one level, functions as a universal remote control for a home entertainment system. The interface of this device, however, is designed in such a way that it may unobtrusively promote a reduction in the user’s television viewing while encouraging an increase in the frequency and quantity of non-sedentary activities. The design of demonstrates how a variety of behavioral science strategies for motivating behavior change can be carefully woven into the operation of a common consumer electronic device. Results of an exploratory evaluation of a single participant using the system in an instrumented home facility are presented.

We approach the design of ubiquitous computing systems in the urban environment as integral to urban design. To understand the city as a system encompassing physical and digital forms and their relationships with people’s behaviours, we are developing, applying and refining methods of observing, recording, modelling and analysing the city, physically, digitally and socially. We draw on established methods used in the space syntax approach to urban design. Here we describe how we have combined scanning for discoverable Bluetooth devices with two such methods, gatecounts and static snapshots. We report our experiences in developing, field testing and refining these augmented methods. We present initial findings on the Bluetooth landscape in a city in terms of patterns of Bluetooth presence and Bluetooth naming practices.

Real world recommendation systems, personalized mobile search, and online city guides could all benefit from data on personal place preferences. However, collecting explicit rating data of locations as users travel from place to place is impractical. This paper investigates the relationship between explicit place ratings and implicit aspects of travel behavior such as visit frequency and travel time. We conducted a four-week study with 16 participants using a novel sensor-based experience sampling tool, called My Experience (Me), which we developed for mobile phones. Over the course of the study Me was used to collect 3,458 in-situ questionnaires on 1,981 place visits. Our results show that, first, sensor-triggered experience sampling is a useful methodology for collecting targeted information in situ. Second, despite the complexities underlying travel routines and visit behavior, there exist positive correlations between place preference and automatically detectable features like visit frequency and travel time. And, third, we found that when combined, visit frequency and travel time result in stronger correlations with place rating than when measured individually. Finally, we found no significant difference in place ratings due to the presence of others.