The tutorials will be held in the afternoon of 20 May 2010 (halfday).
- Bob Kummerfeld, University of Sydney, Australia
- Thomas Strang, German Aerospace Center, Germany
Abstract: Pervasive computing weaves computing into our everyday environments and devices, necessitating an understanding of people's needs and reactions to new technology. The field of human computer interaction (HCI), drawing from other fields including psychology and anthropology, has developed numerous approaches to understanding how people interact with technology. These methods include user studies, focus groups, ethnography, and heuristic evaluations. In an interactive session, this tutorial will provide an introduction to different kinds of user studies with a focus on lab and field studies. The tutorial will discuss how to choose the appropriate study for your research question, practical matters in planning studies, and techniques for analyzing and presenting data collected during user studies. Drawing on examples, the tutorial will also highlight mistakes to avoid and characteristics of successful user studies.
Short biography: A.J. Brush is a researcher at Microsoft Research in Redmond, Washington, USA. Her main research interest is human-computer interaction with a focus on computer supported collaborative work (CSCW) and ubiquitous computing. She enjoys investigating how technology can help people and families with everyday problems including coordination, awareness, and energy conservation. A.J. graduated Summa cum Laude from Williams College and then earned her Ph.D. in Computer Science at the University of Washington. She just completed a three year term as the ACM SIGCHI VP for Membership and Communications and serving as the program co-chair for the Pervasive 2009 conference. She has served on Program Committees for numerous conferences including UbiComp, Pervasive, and Computer Supported Collaborative Work (CSCW), and currently serves on the editorial advisory board of the International Journal of Human-Computer Interaction.
Abstract: Location is a large element of context in ubiquitous computing. There are several technologies and systems for computing location to a varying degree of accuracy. We'll outline some examples of location systems and consider their strengths and weaknesses in terms of cost, accuracy, deployment,calibration, and maintenance. The goal will be to provide researchers with advice that will make them better informed at integrating location systems into ubiquitous computing applications. For those wanting to do research in location systems themselves we aim to provide an overview of the challenges currently being faced in this field.
Short biography: Shwetak N. Patel is an Assistant Professor in the departments of Computer Science and Engineering and Electrical Engineering at the University of Washington. His research interests are in the areas of Human-Computer Interaction, Ubiquitous Computing, and User Interface Software and Technology. He is particularly interested in developing easy-to-deploy sensing technologies and approaches for location and activity recognition applications. Dr. Patel was also the co-founder of Usenso, Inc., a recently acquired demand side energy monitoring solutions provider. He received his Ph.D. in Computer Science from the Georgia Institute of Technology in 2008 and B.S. in Computer Science in 2003. He was also the Assistant Director of the Aware Home Research Initiative at Georgia Tech. Dr. Patel was recently a TR-35 award recipient for 2009.
Abstract:Sequential sensors are those that product a sequence of sensor readings of the same entity over time, such as GPS and accelerometers. Measurements from sensors like these are important for pervasive computing, because they are used to infer a person's context. Unfortunately, sensors are never perfect in terms of noise or accuracy, and they often do not measure the state variables we really need. This tutorial is aimed at introducing fundamental techniques for processing sequential sensor data to reduce noise and infer context beyond what the sensor actually measures. The techniques discussed are not necessarily on the cutting edge of signal processing, but they are well-accepted approaches that have proven to be fundamentally useful in pervasive computing research. Specifically, this tutorial discusses mean and median filters, the Kalman filter, the particle filter, and the hidden Markov model (HMM). Each of these techniques processes sequential sensor data, but they all have different assumptions and representations, which are highlighted to help you make an intelligent choice. The techniques will be illustrated with a running example.
Short biography: John Krumm is a senior researcher at Microsoft Research in Redmond, Washington, USA. He graduated from Carnegie Mellon University with a PhD in robotics and a thesis on texture analysis in images. He worked at the Robotics Center of Sandia National Laboratories in Albuquerque, New Mexico for the next four years. His main projects there were computer vision for object recognition for robots and occupant detection for cars. Since 1997 he has been a researcher at Microsoft Research concentrating on location tracking of people and devices and on ways of using location data to benefit the user. He is a member of ACM and IEEE, holds 37 U.S. patents, and serves on the editorial board the Journal of Location Based Services.
Abstract: Building ubicomp systems is essential to the progress of the field as a whole. Experimentally prototyping ubicomp systems enables us to experience them, discover what they are like to use and reason about core precepts such as the boundaries of the system, its invisibility, the role of its users and the degree of artificial intelligence endemic to it. By implementing systems we discover what comprises ubicomp systems, what is and is not computationally tractable, form hypotheses to be tested and uncover the research challenges that underpin and inform the evolving vision of ubicomp itself. In this tutorial we present a design rationale and process for creating 'good' ubicomp systems, drawing on a number of case studies from the literature and personal experience. We offer consolidated tips on what to look for when deploying ubicomp systems 'in the wild'. This is aimed at sensitising researchers in the space to issues that may face you in the design, implementation, deployment and evaluation stages of your projects. It is our profound hope that you will be able to more quickly design, build, deploy and evaluate your ubicomp system, avoiding many of the common pitfalls we have experienced over the years; moving the science of ubicomp systems development forward more rapidly.
Short biography: Adrian Friday is a Senior Lecturer in the Computing Department at Lancaster University and an active researcher with over 15 years experience in developing systems to support Mobile and Ubiquitous computing. He has extensive experience of collaborative and multi-disciplinary research and was one of the Principal Investigators in Equator, a high-impact UK-wide interdisciplinary initiative, 2001-7. A focus of his work is deployment with end-users, including creating a test-bed of 30 situated public displays on campus (eCampus), which has led to over £465K and €1.4m of new grants, produced two patents (US and UK) and 2 invention disclosures. Adrian is widely published and cited in the international research community: with over 95 peer-reviewed articles; his 5 highest cited publications attract over 1,500 citations (Google Scholar). He has published in the areas of context-aware mobile computing (GUIDE, EU/IST SMS), location privacy and activity recognition. Adrian has been recognised in leading roles in the community, including TPC chair of the most highly respected Ubicomp conferences, Ubicomp 2006 and Pervasive 2009, area editor for SIGMOBILE MC2R, PC member ACM MobiSys 2006, and long term involvement in the premier international mobile computing workshop series IEEE WMCSA/ HotMobile (TPC 2002, 2003, 2006, 2007, chair in 2004, and steering committee 2004-2010).