Ubicomp 2008

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Seoul

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Ubicomp Accepted Papers

The Heterogenous Home

Ryan Aipperspach, University of California, Berkeley
Ben Hooker, Intel Research Berkley
Allison Woodruff, Intel Research Berkeley

"Due to several recent trends, the domestic environment has become more homogeneous and undifferentiated. Drawing on concepts from environmental psychology, we critique these trends. We propose heterogeneity as a new framework for domestic design, and we present design sketches that illustrate how ubiquitous computing technologies can interact with the domestic environment to create a more varied and restorative environment. This work speaks to a number of core issues in ubiquitous computing, such as how the increased presence of devices impacts quality of life, the desirability or undesirability of ubiquitous temporal and spatial availability of devices, and the advantages and disadvantages of device convergence (""all-in-one"" devices) versus device proliferation (single application devices)."

A Context-aware Patient Safety System for the Operating Room

Jakob Bardram, IT University of Copenhagen
Niels N¯rskov, IT University of Copenhagen

"Most context-aware systems have been designed for non-safety-critical environments such as offices, museums, and university campuses. This paper argues that context-awareness can be used for safety-critical systems too. But since the consequences of errors or failures in safety-critical systems are potentially severe, we should have a high degree of confidence in these systems. We present the design, implementation, and evaluation of a context-aware patient safety and information system (CAPSIS) designed for use during surgery. Specifically, our study indicates that CAPSIS could improve patient safety in the operating room. More generally, the paper suggests that context-aware technologies offer a promising step forward in the design of safety-critical systems. "

It's in Your Eyes - Towards Context-Awareness and Mobile HCI Using Wearable EOG Goggles

Andreas Bulling, ETH Zurich
Daniel Roggen, ETH Zurich
Gerhard Trˆster, ETH Zurich

"In this work we describe the design, implementation and evaluation of a novel eye tracker for context-awareness and mobile HCI applications. In contrast to common systems using video cameras, this compact device relies on Electrooculography (EOG). It consists of goggles with dry electrodes integrated into the frame and a small pocket-worn component with a DSP for real-time EOG signal processing. The device is intended for wearable and standalone use: It can store data locally for long-term recordings or stream processed EOG signals to a remote device over Bluetooth. We describe how eye gestures can be efficiently recognised from EOG signals for HCI purposes. In an experiment conducted with 11 subjects playing a computer game we show that 8 eye gestures of varying complexity can be continuously recognised with equal performance to a state-of-the-art video-based system. Physical activity leads to artefacts in the EOG signal. We describe how these artefacts can be removed using an adaptive filtering scheme and characterise this approach on a 5-subject dataset. In addition to explicit eye movements for HCI, we discuss how the analysis of unconscious eye movements may eventually allow to deduce information on user activity and context not available with current sensing modalities."

Getting to Green: Understanding Resource Consumption in the Home

Marshini Chetty, Georgia Institute of Technology
David Tran, Georgia Institute of Technology
Rebecca E. Grinter, Georgia Institute of Technology

"Rising global energy demands, increasing costs and limited natural resources mean that householders are more conscious about managing their domestic resource consumption. Yet, the question of what tools Ubicomp researchers can create for residential resource management remains open. To begin to address this omission, we present a qualitative study of 15 households and their current management practices around the water, electricity and natural gas systems in the home. We find that in-the-moment resource consumption is mostly invisible to householders and that they desire more real-time information to help them save money, keep their homes comfortable and be environmentally friendly. Designing for domestic sustainability therefore turns on improving the visibility of resource production and consumption costs as well as supporting both individuals and collectives in behavior change. Domestic sustainability also highlights the caveat of potentially creating a green divide by making resource management available only to those who can afford the technologies to support being green. Finally, we suggest that the Ubicomp community can contribute to the domestic and broader sustainability agenda by incorporating green values in designs and highlight the challenge of collecting data on being green."

"Flowers or a Robot Army? Encouraging Awareness & Activity with Personal, Mobile Displays"

Sunny Consolvo, Intel Research Seattle, University of Washington
Predrag Klasnja, University of Washington
David W. McDonald, University of Washington
Daniel Avrahami, Intel Research Seattle
Jon Froehlich, University of Washington
Louis LeGrand, Intel Research Seattle
Ryan Libby, University of Washington
Keith Mosher, University of Washington
James A, Landay, University of Washington

"Personal, mobile displays, such as those on mobile phones, are ubiquitous, yet for the most part, underutilized. We present results from a field experiment that investigated the effectiveness of these displays as a means for improving awareness of daily life (in our case, self-monitoring of physical activity). Twenty-eight participants in three experimental conditions used our UbiFit system for a period of three months in their day-to-day lives over the winter holiday season. Our results show, for example, that participants who had an awareness display were able to maintain their physical activity level (even during the holidays), while the level of physical activity for participants who did not have an awareness display dropped significantly. We discuss our results and their general implications for the use of everyday mobile devices as awareness displays."

The Potential for Location-Aware Power Management

Robert Harle, University of Cambridge
Andy Hopper, University of Cambridge

"This paper explores the use of location-awareness to dynamically optimise the energy consumption of an office. It makes use of high-accuracy location data collected over 60 days randomly selected from a year in a commercial environment to evaluate the potential for energy savings and to motivate techniques that might be used. The results suggest that the energy expended on lighting and fast-response systems could have been cut by 50 %; that 75.8 % of the average user's working day was spent in their office; and that around 140Wh per PC per day could have been saved, compared to a policy that had machines on for the entirety of the working day. We also find inconsistent office usage that would make optimising slow response systems much harder. "

Using Visualizations to Increase Compliance in Experience Sampling [NOTE]

Gary Hsieh, Carnegie Mellon University
Ian Li, Carnegie Mellon University
Anind Dey, Carnegie Mellon University
Jodi Forlizzi, Carnegie Mellon University
Scott Hudson, Carnegie Mellon University

"Experience sampling method (or ESM) is a common data collection method to understand user behavior and to evaluate ubiquitous computing technologies. However, ESM studies often demand too much time and commitment from participants, which leads to attrition and low compliance among participants. We introduce a new approach called experience sampling with feedback or ES+feedback that improves compliance by giving feedback to participants through various visualizations. Providing feedback to users makes the information personally relevant and increases the value of the study to participants, which increases their compliance. Our exploratory study shows that ES+feedback increases the compliance rate by 23%."

Real World Activity Recognition with Multiple Goals

Derek Hao Hu, Hong Kong University of Science and Technology
Sinno Jialin Pan, Hong Kong University of Science and Technology
Vincent Wenchen Zheng, Hong Kong University of Science and Technology
Nathan Nan Liu, Hong Kong University of Science and Technology
Qiang Yang, Hong Kong University of Science and Technology

"Recognizing and understanding the activities of people from sensor readings is an important task in ubiquitous computing. Activity recognition is also a particularly difficult task because of the inherent uncertainty and complexity of the data collected by the sensors. Many researchers have tackled this problem in an overly simplistic setting by assuming that users often carry out single activities one at a time or multiple activities consecutively, one after another. However, so far there has been no formal exploration on the degree in which humans perform concurrent or interleaving activities, and no thorough study on how to detect emph{multiple} goals in a real world scenario. In this article, we ask the fundamental questions of whether users often carry out multiple concurrent and interleaving activities or single activities in their daily life, and if so, whether such complex behavior can be detected accurately using sensors. We define several classes of complexity levels under a goal taxonomy that describe different granularities of activities, and relate the recognition accuracy with different complexity levels or granularities. We present a theoretical framework for recognizing multiple concurrent and interleaving activities, and evaluate the framework in several real-world ubiquitous computing environments. "

Discovery of Activity Patterns using Topic Models

T‚m Huynh, TU Darmstadt
Mario Fritz, TU Darmstadt
Bernt Schiele, TU Darmstadt

In this work we propose a novel method to recognize daily routines as a probabilistic combination of activity patterns. The use of topic models enables the automatic discovery of such patterns in a user's daily routine. We report experimental results that show the ability of the approach to model and recognize daily routines without user annotation.

Who will be the customer?: A social robot that anticipates peopleís behavior from their trajectories

Takayuki Kanda, ATR
Dylan Glas, ATR
Masahiro Shiomi, ATR
Hiroshi Ishiguro, ATR
Norihiro Hagita, ATR

"For a robot providing services to people in a public space such as a train station or a shopping mall, it is important to distinguish potential customers, such as window-shoppers, from other people, such as busy commuters. In this paper, we present a series of techniques for anticipating peopleís behavior in a public space, mainly based on the analysis of accumulated trajectories, and we demonstrate the use of these techniques in a social robot. We placed a ubiquitous sensor network consisting of six laser range finders in a shopping arcade. The system tracks peopleís positions as well as their local behaviors such as fast walking, idle walk-ing, or stopping. We accumulated peopleís trajectories for a week, applying a clustering technique to the accumulated trajectories to extract information about the use of space and peopleís typical global behaviors. This information enables the robot to target its services to people who are walking idly or stopping. The robot anticipates both the areas in which people are likely to perform these behaviors, and also the probable local behaviors of individuals a few seconds in the future. In a field experiment we demonstrate that this system enables the robot to serve people efficiently."

Deploy Spontaneously: Supporting End-Users in Building and Enhancing a Smart Home

Fahim Kawsar, Waseda University
Kaori Fujinami, Tokyo University of Agriculture and Technology
Tatsuo Nakajima, Waseda University

"This paper explores system issues for involving end users in constructing and enhancing a smart home. In support of this involvement we present an infrastructure and a tangible deployment tool. Active participation of users is essential in a domestic environment as it offers simplicity, greater user-centric control, lower deployment costs and better support for personalization. Our proposed infrastructure provides the foundation for end user deployment utilizing a loosely coupled framework to represent an artefact and its augmented functionalities. Pervasive applications are built independently and are expressed as a collection of functional tasks. A runtime component, FedNet maps these tasks to corresponding service provider artefacts. The tangible deployment tool uses FedNet and allows end users to deploy and control artefacts and applications only by manipulating RFID cards. Primary advantages of our approach are two-fold. Firstly, it allows end users to deploy ubicomp systems easily in a ""Do-it-Yourself"" fashion. Secondly, it allows developers to write applications and to build augmented artefacts in a generic way regardless of the constraints of the target environment. We describe an implemented prototype and illustrate its feasibility in a real life deployment session by the end users. Our study shows that the end users might be involved in deploying future ubicomp systems if appropriate tools and supporting infrastructure are provided. "

Using Wearable Sensors and Real Time Inference to Understand Human Recall of Routine Activities

Predrag Klasnja, Intel Research Seattle
Beverly L. Harrison, Intel Research Seattle
Louis LeGrand, Intel Research Seattle
Anthony LaMarca, Intel Research Seattle
Jon Froehlich, University of Washington
Scott E. Hudson, Carnegie Mellon University

"Usersí ability to accurately recall frequent, habitual activities is fundamental to a number of disciplines, from health sciences to machine learning. However, few, if any, studies exist that have assessed optimal sampling strategies for in situ self-reports. In addition, few technologies exist that facilitate benchmarking self-report accuracy for routine activities. We report on a study investigating the effect of sampling frequency of self-reports of two routine activities (sitting and walking) on recall accuracy and annoyance. We used a novel wearable sensor platform that runs a real time activity inference engine to collect in situ ground truth. Our results suggest that a sampling frequency of five to eight times per day may yield an optimal balance of recall and annoyance. Additionally, requesting self-reports at regular, predetermined times increases accuracy while minimizing perceived annoyance since it allows participants to anticipate these requests. We discuss our results and their implications for future studies. "

Localization In Mobile Ad Hoc Networks Using Cumulative Route Information

Jahyoung Koo, Department of Computer Science, Yonsei University
Jiyoung Yi, Department of Computer Science, Yonsei University
Hojung Cha, Department of Computer Science, Yonsei University

"Discovering the location of the mobile nodes carried by people is important issue for many sensor applications. Several localization techniques have been proposed, but human mobility patterns and collaboration between mobile nodes have been seldom considered. In this paper, we propose a mobile node localization system based on collaboration and route information that characterizes human mobility. To validate the feasibility of our approach, the proposed system is implemented and experiments are conducted on real routes and to evaluate various scenarios, simulation experiment was also conducted."

Designing Sociable IT for Public Use

Steinar Kristoffersen, ÿstfold University College
Ingunn Bratteberg, Mamut ASA

"Service providers increasingly use self-service systems, such as kiosk and automata that offer faster and more flexible service. Most of us are familiar with appliances for buying and validating tickets, purchasing soft drinks or getting the newspaper. We book tables in restaurants and hire cars using hotel lobby kiosks. Unfortunately, many such systems confuse and annoy their users. Thus, information technology design for the public space poses distinct challenges. Yet, it is relatively unmapped within our field. Based on an ethnographic study of the purchase and validation of ticketless travel for an airport train, this paper shows how such systems need an extended framework of usability principles, which goes beyond well-known interaction design guidelines. "

Dealing with Sensor Displacement in Motion-Based Onbody Activity Recognition

Kai Kunze, University Passau
Paul Lukowicz, University Passau

" We present a set of heuristics that significantly increase the robustness of motion sensor based activity recognition with respect to sensor displacement. In this paper placement refers to the position within a single body part (e.g lower arm). We show how, within certain limits and with modest quality degradation, motion sensor based activity recognition can be implemented in a displacement tolerant way. We first describe the physical principles that lead to our heuristic. We then evaluate them first on a set of synthetic lower arm motions which are well suited to illustrate the strengths and limits of our approach, then on an extended modes of locomotion problem (sensors on the upper leg) and finally on a set of exercises performed on various gym machines (sensors placed on the lower arm). In this example our heuristic raises the displaced recognition rate from 24 % of a displaced accelerometer, which had 96 % recognition when not displaced, to 82 %."

"Interactionist AI and the promise of ubicomp, or, how to put your box in the world without putting the world in your box"

Lucian Leahu, Cornell University
Phoebe Sengers, Cornell University
Michael Mateas, University of California, Santa Cruz

"In many ways, the central problem of ubiquitous computing ñ how computational systems can make sense of and respond sensibly to a complex, dynamic environment laden with human meaning ñ is identical to that of Artificial Intelligence (AI). Indeed, some of the central challenges that ubicomp currently faces in moving from prototypes that work in restricted environments to the complexity of real-world environments ñ e.g. difficulties in scalability, integration, and fully formalizing context ñ echo some of the major issues that have challenged AI researchers over the history of their field. In this paper, we explore a key moment in AIís history where researchers grappled directly with these issues, resulting in a variety of novel technical solutions within AI. We critically reflect on six strategies from this history to suggest technical solutions for how to approach the challenge of building real-world, usable solutions in ubicomp today."

Lifelogging Memory Appliance for People with Episodic Memory Impairment

Matthew Lee, Carnegie Mellon
Anind Dey, Carnegie Mellon

"Lifelogging technologies have the potential to provide memory cues for people who struggle with episodic memory impairment (EMI). These memory cues enable the recollection of significant experiences, which is important for people with EMI to regain a sense of normalcy in their lives. However, lifelogging technologies often collect an overwhelmingly large amount of data to review. The best memory cues need to be extracted and presented in a way that best supports episodic recollection. We describe the design of a new lifelogging system that captures photos, ambient audio, and location information and leverages both automated content/context analysis and the expertise of family caregivers to facilitate the extraction and annotation of a salient summary consisting of good cues from the lifelog. The system presents the selected cues for review in a way that maximizes the opportunities for the person with EMI to think deeply about these cues to trigger memory recollection on his own without burdening the caregiver. We compare our system with another review system that requires the caregiver to repeatedly guide the review process. Our self-guided system resulted in better memory retention and imposed a smaller burden on the caregiver whereas the caregiver-guided approach provided more opportunities for caregiver interaction."

Design and Implementation of a Secure Wireless Mote-Based Medical Sensor Network

Kriangsiri Malasri, University of Memphis
Lan Wang, University of Memphis

"A medical sensor network can wirelessly monitor vital signs of humans, making it useful for long-term health care without sacrificing patient comfort and mobility. For such a network to be viable, its design must protect data privacy and authenticity given that medical data are highly sensitive. We identify the unique security challenges facing such a sensor network and propose a set of resource-efficient mechanisms to address these challenges. Our solution includes (1) a novel two-tier scheme for verifying the authenticity of patient data; (2) an ECC-based secure key exchange protocol to set up shared keys between sensor nodes and base stations; and (3) symmetric encryption/decryption for protecting data confidentiality and integrity. We have implemented the proposed mechanisms on a wireless mote platform and our results confirm their feasibility."

Enhanced Shopping: A Dynamic Map in a Retail Store [NOTE]

Alexander Meschtscherjakov, University of Salzburg
Wolfgang Reitberger, University of Salzburg
Michael Lankes, University of Salzburg
Manfred Tscheligi, University of Salzburg

"This article investigates the prototypical implementation of a dynamic map of a retail store and the results of an empirical study in the shopping environment. Due to the distinct characteristics of the context of shopping (e.g. openness to the public, preexisting technologies), this context is particularly fruitful for UbiComp technologies. The prototype consists of a display showing an enhanced store map, which combines the dynamic visualization of customer activity (e.g. hot-spots, sales ranks) with conventional map elements (e.g. product locations, promotions). The results of our three-day in-situ study indicate the relevance and the usefulness of UbiComp technologies for shopping environments."

Improving the Recognition of Interleaved Activities [NOTE]

Joseph Modayil, University of Rochester
Tongxin Bai, University of Rochester
Henry Kautz, University of Rochester

"We introduce Interleaved Hidden Markov Models for recognizing multitasked activities. The model captures both inter-activity and intra-activity dynamics. Although the state space is intractably large, we describe an approximation that is both effective and efficient. This method significantly reduces the error rate when compared with previously proposed methods. The algorithm is suitable for mobile platforms where computational resources may be limited."

An Empirical Investigation of Concerns of Everyday Tracking and Recording Technologies

David Nguyen, UC Irvine
Alfred Kobsa, UC Irvine
Gillian Hayes, UC Irvine

"This paper presents an exploration and analysis of attitudes towards everyday tracking and recording technologies (e.g., credit cards, store loyalty cards, store video cameras). Interview participants reported being highly concerned with information privacy. At the same time, however, they also reported being significantly less concerned regarding the use of everyday technologies that have the capabilities to collect, process, and disseminate personal information. We present results from this study that both identify and begin to explain this discrepancy."

On Using Existing Time-Use Study Data for Ubiquitous Computing Applications

Kurt Partridge, PARC
Philippe Golle, PARC

"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."

Online Everywhere: Evolving Mobile Instant Messaging Practices

Donald J. Patterson, University of California, Irvine
Christopher Baker, University of California, Irvine
Xianghua Ding, University of California, Irvine
Samuel J. Kaufman, University of California, Irvine
Kah Liu, University of California, Irvine
Andrew Zaldivar, University of California, Irvine

"In this paper we report on the results of a large scale user survey investigating the status setting and interruption management behavior of mobile instant messaging (IM) users with existing systems. The motivation for this study was to inform the design of interface tools that support users by setting contextually appropriate awareness messages. Our results demonstrate that many desktop IM practices have been appropriated by mobile laptop users, but in the face of increasingly situated computer usage and an ""always online"" culture, several frictions are emerging between desktop and mobile practices. We find that common assumptions about IM users and the established awareness cues are failing and users are frequently embarrassed and interrupted with negative and sometimes threatening consequences. "

Reflecting on the Invisible: Understanding End-User Perceptions of Ubiquitous Computing

Erika Shehan Poole, Georgia Institute of Technology
Christopher A. Le Dantec, Georgia Institute of Technology
James Eagan, Georgia Institute of Technology
W. Keith Edwards, Georgia Institute of Technology

"How can designers of ubiquitous computing technologies ensure that they understand the non-functional needs, values, and expectations of end-users? In this paper, we use a qualitative method from public policy to elicit reflective feedback from end-users about technologies that they may not yet have used nor fully comprehend. Our study uncovers information about end-user perceptions of RFID, including a range of ìfolk theoriesî held by the public about this technology, and their associations of it with certain social groups and values. We argue that these perceptions can limit technological adoption, and conclude with a discussion of challenges for the design and deployment of ubiquitous computing systems."

Living with the Tableau Machine: A Longitudinal Investigation of a Curious Domestic Intelligence

Zachary Pousman, Georgia Institute of Technology
Mario Romero, Georgia Institute of Technology
Adam Smith, University of California, Santa Cruz
Michael Mateas, University of California, Santa Cruz

"We present a longitudinal investigation of Tableau Machine, an intelligent entity that interprets and reflects the lives of occupants in the home. We created Tableau Machine (TM) to explore the parts of home life that are unrelated to accomplishing tasks. Task support for ìsmart homesî has inspired many researchers in the community. We consider design for experience, an orthogonal dimension to task-centric home life. TM produces abstract visualizations on a large LCD every few minutes, driven by a set of four overhead cameras that capture a sense of the social life of a domestic space. The openness and ambiguity of TM allow for a cycle of co-interpretation with householders. We report on three longitudinal deployments of TM for a period of six weeks. Participant families engaged with TM at the outset to understand how their behaviors were influencing the machine, and, while TM remained puzzling, householders interacted richly with TM and its images. We extract some key design implications for an experience-focused smart home."

MobiRate: Making Mobile Raters Stick to their Word

Daniele Quercia, University College London
Stephen Hailes, University College London
Licia Capra, University College London

"To share services, portable devices may need to locate reputable in-range providers and, to do so, they may exchange ratings with each other. However, providers may well tweak ratings to their own advantage. That is why we have designed a new decentralized mechanism (dubbed MobiRate) with which portable devices store ratings in (local) tamper-evident tables and check the integrity of those tables through a gossiping protocol. We evaluate the extent to which MobiRate reduces the impact of tampered ratings and consequently locates reputable service providers. We do so using real mobility and social network data. We also assess computational and communication costs of MobiRate on mobile phones. "

Plastic: A Metaphor for Integrated Technologies

Tye Rattenbury, People and Practices Research Group
Dawn Nafus, People and Practices Research Group
Ken Anderson, People and Practices Research Group

"Ubiquitous computing research has recently focused on ëbusynessí in American households. While these projects have generated important insights into coordination and communication, we think they overlook the more spontaneous and opportunistic activities that surround and support the scheduled ones. Using data from our mixed-methods study of notebook and ultra-mobile PC use, we argue for a different perspective based on a metaphor of ëplasticí. ëPlasticí captures the way technologies, specifically computers, have integrated into the heterogeneous rhythms of daily life. Plastic technologies harmonize with and support daily life by filling opportunistic gaps, shrinking and expanding until interrupted, not demanding conscious coordination, supporting multitasking, and by deferring to external contingencies."

CILoS: A CDMA Indoor Localization System

Waqas ur Rehman, University of Toronto
Eyal de Lara , University of Toronto
Stefan Saroiu, University of Toronto

"CILoS is an indoor localization system based on CDMA mobile phone signal fingerprinting. CDMA networks vary their transmission power to accommodate fluctuations in network load. This affects signal intensity and therefore limits the practicality of traditional fingerprinting approaches based on receiver signal strength (RSSI) measurements. Instead, CILoS uses fingerprints of signal delay that are robust to cell resizing. We demonstrate that CILoS achieves a median accuracy of 5 meters, and compares favourably to RSSI fingerprinting systems. We highlight the significance of wide fingerprints, constructed through scanning multiple channels, for achieving high localization accuracy. We also show that our system can accurately differentiate between floors of a multifloor building."

Spyn: Augmenting Handcraft to Support Storytelling and Reflection

Daniela Rosner, School of Information UC Berkeley
Kimiko Ryokai, School of Information Berkeley Center for New Media UC Berkeley

"Ubicomp research has spurred the exploration of more ìnaturalî or ìinvisibleî interfaces that can be seamlessly embedded into their environment. In this paper, we discuss the role such technology can play in augmenting existing creative practice to enhance the sharing of the handcraft process. We present the design and implementation of Spyn, a system for knitters to record, playback, and share information involved in the creation of their hand-knit artifacts. Guided by a formative study of knitting practices, we designed Spyn to capture information while a person knits and allow for the subsequent retrieval of the information using the knit artifact. Spyn uses computer vision techniques in combination with patterns of infrared ink printed on yarn to correlate locations in knit fabric with messages recorded during the knitting process. Rather than seeking to improve the speed or accuracy of the knitter, we designed Spyn to enrich the knitterís craft while preserving the look and feel of the knit artifact."

Mixed-Initiative Conflict Resolution for Context-aware Applications

Choonsung Shin, GIST U-VR Lab.
Anind K. Dey, Human-Computer Interaction Institute, Carnegie Mellon University
Woontack Woo, GIST U-VR Lab.

"A number of technologies have contributed to automatically resolving resource conflicts between multiple users in a smart space. However, such systems eliminate the usersí ability to perform this conflict resolution by themselves, which they actually prefer to do in certain circumstances. Since both resolution approaches have their merits, we propose a mixed-initiative conflict resolution system, which combines automatic conflict resolution with mediated, or user-driven, resolution by exploiting contextual information in context-aware applications. An evaluation of our system found that users prefer to use a mediated resolution approach when their preferences about outcome are very different from othersí, but have no preferred method when their preferences about outcome are similar to othersí."

Protecting your Daily In-Home Activity Information from a Wireless Snooping Attack

Vijay Srinivasan, University of Virginia
John Stankovic, University of Virginia
Kamin Whitehouse, University of Virginia

"In this paper, we first present a new privacy leak in residential wireless ubiquitous computing systems, and then we propose guidelines for designing future systems to prevent this problem. We show that we can observe private activities in the home such as cooking, showering, toileting, and sleeping by eavesdropping on the wireless transmissions of sensors in a home, even when all of the transmissions are encrypted. We call this the Fingerprint and Timing-based Snooping (FATS) attack. This attack can already be carried out on millions of homes today, and may become more important as ubiquitous computing environments such as smart homes and assisted living facilities become more prevalent. In this paper, we demonstrate and evaluate the FATS attack on eight different homes containing wireless sensors. We also propose and evaluate a set of privacy preserving design guidelines for future wireless ubiquitous systems and show how these guidelines can be used in a hybrid fashion to prevent against the FATS attack with low implementation costs."

Accessible Contextual Information for Urban Navigation [NOTE]

Jason Stewart, University of Michigan
Sara Baumann, University of Michigan
Michelle Escobar, University of Michigan
Jakob Hilden, University of Michigan
Kumud Bihani, University of Michigan
Mark W. Newman, University of Michigan

"We present Talking Points, an urban orientation system based on the idea that an individual's walking journey can be enhanced by providing contextual information about points of interest (POIs) along their route. Our formative research revealed numerous ways to provide serendipitous and task-critical information for both sighted and visually impaired users as they navigate through an urban environment on foot. Based on this, we developed a prototype system comprised of the following: an unobtrusive mobile device to present the user with contextual information; a socially maintained online database containing information about POIs; software that is accessible via both a graphical and a speech user interface; and location ""tags"" to be detected by the unobtrusive device. This socially maintained urban orientation and contextual information system offers relevant, dynamic, and up-to-date information, a combination which may not otherwise be accessible."

Wideband PowerLine Positioning for Indoor Localization

Erich Stuntebeck, Georgia Institute of Technology
Shwetak Patel, Georgia Institute of Technology
Thomas Robertson, Georgia Institute of Technology
Matthew Reynolds, Duke University
Gregory Abowd, Georgia Institute of Technology

"Fingerprinting techniques for indoor localization have been widely explored. A particular approach by Patel et al. suggested leveraging of the residential powerline as the signaling mechanism for a domestic location capability. In this paper, we critically examine that initial work, called powerline positioning (PLP). We find the proposed technique lacking in temporal stability, requiring frequent and undesired recalibration in some environments. We also determine that there is no a priori method to determine a pair of signaling frequencies that will reliably work in any space. We propose a wideband approach to PLP (WPLP) that injects up to 44 different frequencies into the powerline. We show that this WPLP approach improves upon overall positioning accuracy, demonstrates greatly improved temporal stability and has the added advantage of working in commercial indoor spaces."

Accurate Activity Recognition in a Home Setting

Tim van Kasteren, University of Amsterdam
Athanasios Noulas, University of Amsterdam
Gwenn Englebienne, University of Amsterdam
Ben Krose, University of Amsterdam

"A sensor system capable of automatically recognizing activities would allow many potential ubiquitous applications. In this paper, we present an easy to install sensor network and an accurate but inexpensive annotation method. A recorded dataset consisting of 28 days of sensor data and its annotation is described and made available to the community. Through a number of experiments we show how the hidden Markov model and conditional random fields perform in recognizing activities. We achieve a timeslice accuracy of 95.6% and a class accuracy of 79.4%."

Picture This! Film assembly using toy gestures

Cati Vaucelle, MIT Media Laboratory
Hiroshi Ishii, MIT Media Laboratory

"We present Picture This! a new input device embedded in childrenís toys for video composition. It consists of a new form of interaction for childrenís capturing of storytelling with physical artifacts. It functions as a video and storytelling performance system in that children craft videos with and about character toys as the system analyzes their gestures and play patterns. Childrenís favorite props alternate between characters and cameramen in a film. As they play with the toys to act out a story, they conduct film assembly. We position our work as ubiquitous computing that supports childrenís tangible interaction with digital materials. During user testing, we observed children ages 4 to 10 playing with Picture This!. We assess to what extent gesture interaction with objects for video editing allows children to explore visual perspectives in storytelling. A new genre of Gesture Object Interfaces as exemplified by Picture This relies on the analysis of gestures coupled with objects to represent bits."

Bookisheet: Bendable Device for Browsing Content Using the Metaphor of Leafing Through the Pages

Jun-ichiro Watanabe, Hitachi Human Interaction Laboratory, Hitachi Ltd.
Arito Mochizuki, Hitachi Human Interaction Laboratory, Hitachi Ltd.
Youichi Horry, Hitachi Human Interaction Laboratory, Hitachi Ltd.

"We have developed an interface for browsing content that uses the metaphor of turning the pages of a book. Using our ìBookisheetî interface, which consists of two thin plastic sheets and bend sensors, a user can easily scroll digital content such as photos by bending one side of the sheet or the other. This action provides the tangible and pleasant sense of turning pages in a book. Bookisheet can be used not only as an interface for conventional information terminals but also as one for flexible displays."

Pedestrian Localisation for Indoor Environments

Oliver Woodman, University of Cambridge
Robert Harle, University of Cambridge

"Location information is an important source of context for ubiquitous computing systems. This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial unit and without knowledge of the userís initial location. We show how to handle multiple floors and stairways, how to handle symmetry in the environment, and how to initialise the localisation algorithm using WiFi signal strength to reduce initial complexity. We evaluate the entire system experimentally, using an independent tracking system for ground truth. Our results show that we can track a user throughout a 8725 square metre building spanning three floors to within 0.5m 75% of the time, and to within 0.73m 95% of the time."

Towards the Automated Social Analysis of Situated Speech Data [NOTE]

Danny Wyatt, University of Washington
Tanzeem Choudhury, Dartmouth College
Jeff Bilmes, University of Washington
James Kitts, Columbia University

"We present an automated approach for studying fine-grained details of social interaction and relationships. Specifically, we analyze the conversational characteristics of a group of 24 individuals over a six-month period, explore the relationship between conversational dynamics and network position, and identify behavioral correlates of tie strengths within a network. The ability to study conversational dynamics and social networks over long time scales, and to investigate their interplay with rigor, objectivity, and transparency will complement the traditional methods for scientific inquiry into social dynamics. They may also enable socially aware ubiquitous computing systems that are cognizant of and responsive to the user's engagement with her social environment."

Schema Matching for Context-Aware Computing

Wenwei Xue, School of Computing, National University of Singapore
Hungkeng Pung, School of Computing, National University of Singapore
Paulito Palmes, School of Computing, National University of Singapore
Tao Gu, Institute for Infocomm Research, Singapore

"Context-aware computing is a key paradigm of ubiquitous computing in which applications automatically adapt their operations to dynamic context data from multiple sources. Managing a number of distributed sources, a middleware that facilitates the development of context-aware applications must provide a uniform view of all these sources to the applications. Local schemas of context data from individual sources need to be matched into a set of global schemas in the middleware, upon which applications can issue context queries to acquire data. In this paper, we study this problem of schema matching for context-aware computing. We propose a multi-criteria algorithm to determine candidate attribute matches between two schemas. The algorithm adaptively adjusts the priorities of different criteria based on previous matching results to improve the efficiency and accuracy of succeeding operations. We further develop an algorithm to categorize a new local schema into one of the global schemas whenever possible via a shared attribute dictionary. Our results based on schemas from real-world websites demonstrate the good matching accuracy achieved by our algorithms."

A Quantitative Investigation of Inertial Power Harvesting for Human-powered Devices

Jaeseok Yun, Georgia Institute of Technology
Shwetak Patel, University of Washington
Matt Reynolds, Duke University
Gregory Abowd, Georgia Institute of Technology

"We present an empirical study of the long-term practicality of using human motion to generate operating power for body-mounted consumer electronics and health sensors. We have collected a large continuous acceleration dataset from eight experimental subjects going about their normal daily routine for 3 days each. Each subject is instrumented with a data collection apparatus that simultaneously logs 3-axis, 80Hz acceleration data from six body locations. We use this dataset to optimize a first-principles physical model of the commonly used velocity damped resonant generator (VDRG) by selecting physical parameters such as resonant frequency and damping coefficient to maximize harvested power. Our results show that with reasonable assumptions on size, mass, placement, and efficiency of VDRG harvesters, most body-mounted wireless sensors and even some consumer electronics devices, may be powered continuously and indefinitely from everyday motion."

Understanding Mobility Based on GPS Data

Yu Zheng, Microsoft Research Asia
Quannan Li, Microsoft Research Asia
Xing Xie, Microsoft Research Asia
Yukun Chen, Microsoft Research Asia
Wei-Ying Ma, Microsoft Research Asia

"Both recognizing human behavior and understanding a userís mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the userís mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer peopleís motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement."

Navigate Like a Cabbie: Probabilistic Reasoning from Observed Context-Aware Behavior

Brian Ziebart, Carnegie Mellon University
Andrew Maas, Carnegie Mellon University
Anind Dey, Carnegie Mellon University
Drew Bagnell, Carnegie Mellon University

"We present PROCAB, an efficient method for Probabilistically Reasoning from Observed Context-Aware Behavior. It models the context-dependent utilities and underlying reasons that people take different actions. The model generalizes to unseen situations and scales to incorporate rich contextual information. We train our model using the route preferences of 25 taxi drivers demonstrated in over 100,000 miles of collected data, and demonstrate the performance of our model by inferring: (1) decision at next intersection, (2) route to known destination, and (3) destination given partially traveled route."

Locally organized by Sungkyunkwan Univ. and UCN

In cooperation with ACM SIGMOBILE, SIGCHI, SIGSOFT, SIGWEB
The Proceedings will be published by ACM

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