UbiComp 2014 Awards
10 Year Impact
The “10-Year Impact Award” recognizes works from the 2004 conference that, with the benefit of that hindsight, are seen to have had the greatest impact in the field.
The research papers receiving a “10-Year Impact Award” this year were:
In this work, a system for recognizing activities in the home setting using a set of small and simple state-change sensors is introduced. The sensors are designed to be "tape on and forget" devices that can be quickly and ubiquitously installed in home environments. The proposed sensing system presents an alternative to sensors that are sometimes perceived as invasive, such as cameras and microphones. Unlike prior work, the system has been deployed in multiple residential environments with non-researcher occupants. Preliminary results on a small dataset show that it is possible to recognize activities of interest to medical professionals such as toileting, bathing, and grooming with detection accuracies ranging from 25% to 89% depending on the evaluation criteria used.
In this work, algorithms are developed and evaluated to detect physical activities from data acquired using five small biaxial accelerometers worn simultaneously on different parts of the body. Acceleration data was collected from 20 subjects without researcher supervision or observation. Subjects were asked to perform a sequence of everyday tasks but not told specifically where or how to do them. Mean, energy, frequency-domain entropy, and correlation of acceleration data was calculated and several classifiers using these features were tested. Decision tree classifiers showed the best performance recognizing everyday activities with an overall accuracy rate of 84%. The results show that although some activities are recognized well with subject-independent training data, others appear to require subject-specific training data. The results suggest that multiple accelerometers aid in recognition because conjunctions in acceleration feature values can effectively discriminate many activities. With just two biaxial accelerometers – thigh and wrist – the recognition performance dropped only slightly. This is the first work to investigate performance of recognition algorithms with multiple, wire-free accelerometers on 20 activities using datasets annotated by the subjects themselves.
Location estimation is an important part of many ubiquitous computing systems. Particle filters are simulation-based probabilistic approximations which the robotics community has shown to be effective for tracking robots’ positions. This paper presents a case study of applying particle filters to location estimation for ubiquitous computing. Using trace logs from a deployed multi-sensor location system, we show that particle filters can be as accurate as common deterministic algorithms. We also present performance results showing it is practical to run particle filters on devices ranging from high-end servers to handhelds. Finally, we discuss the general advantages of using probabilistic methods in location systems for ubiquitous computing, including the ability to fuse data from different sensor types and to provide probability distributions to higher-level services and applications. Based on this case study, we conclude that particle filters are a good choice to implement location estimation for ubiquitous computing.
Left to Right: Judy Kay (Steering Committee Chair) with 10 Year Impact Award winners
Ling Bao, Emmanuel Munguia Tapia, Jeffrey Hightower, and Gaetano Borriello.
Best Papers
Listen to this paper being presented, and view the slides on Vimeo, here.
Left to Right: James Scott (Program Co-Chair) with Best Paper authors
Elena Oat, Mohit Sethi, and Mario Di Francesco, and Julie Kientz and Junehwa Song (Program Co-Chairs).
The layouts of the buildings we live in shape our everyday lives. In office environments, building spaces affect employees' communication, which is crucial for productivity and innovation. However, accurate measurement of how spatial layouts affect interactions is a major challenge and traditional techniques may not give an objective view. We measure the impact of building spaces on social interactions using wearable sensing devices. We study a single organization that moved between two different buildings, affording a unique opportunity to examine how space alone can affect interactions. The analysis is based on two large scale deployments of wireless sensing technologies: short-range, lightweight RFID tags capable of detecting face-to-face interactions. We analyze the traces to study the impact of the building change on social behavior, which represents a first example of using ubiquitous sensing technology to study how the physical design of two workplaces combines with organizational structure to shape contact patterns.
Listen to this paper being presented, and view the slides on Vimeo, here.
Left to Right: Cecilia Mascolo (Best Paper author), James Scott (Program Co-Chair), Christos Efstratiou (Best Paper author),
Julie Kientz (Program Co-Chair) and Junehwa Song (Program Co-Chair).
In the context of a myriad of mobile apps which collect personally identifiable information (PII) and a prospective market place of personal data, we investigate a user-centric monetary valuation of mobile PII. During a 6-week long user study in a living lab deployment with 60 participants, we collected their daily valuations of 4 categories of mobile PII (communication, e.g. phonecalls made/received, applications, e.g. time spent on different apps, location and media, e.g. photos taken) at three levels of complexity (individual data points, aggregated statistics and processed, i.e. meaningful interpretations of the data). In order to obtain honest valuations, we employ a reverse second price auction mechanism. Our findings show that the most sensitive and valued category of personal information is location. We report statistically significant associations between actual mobile usage, personal dispositions, and bidding behavior. Finally, we outline key implications for the design of mobile services and future markets of personal data.
Listen to this paper being presented, and view the slides on Vimeo, here.
Left to Right: Julie Kientz and Junehwa Song (Program Co-Chairs) with Best Paper authors Blase Ur,
Bruno Lepri, Christos Efstratiou, and Mohit Sethi, and James Scott (Program Co-Chair).
We investigated how household deployment of Internet-connected locks and security cameras could impact teenagers' privacy. In interviews with 13 teenagers and 11 parents, we investigated reactions to audit logs of family members' comings and goings. All parents wanted audit logs with photographs, whereas most teenagers preferred text-only logs or no logs at all. We unpack these attitudes by examining participants' parenting philosophies, concerns, and current monitoring practices. In a follow-up online study, 19 parents configured an Internet-connected lock and camera system they thought might be deployed in their home. All 19 participants chose to monitor their children either through unrestricted access to logs or through real-time notifications of access. We discuss directions for auditing interfaces that could improve home security without impacting privacy.
Listen to this paper being presented, and view the slides on Vimeo, here.
Left to Right: Julie Kientz and Junehwa Song (Program Co-Chairs) with Best Paper authors
Blase Ur and Jaeyeon Jung, and James Scott (Program Co-Chair).
Programming Competition
The UbiComp/ISWC 2014 Programming Competition invited researchers from around the world to analyze Android smartphone usage data collected by the Device Analyzer project.
The winning entry examined everyday interactions between a user and their mobile device. The winners looked at interaction traces from 1,960 Android devices and derived a new model of user interaction sessions. The model separates out sessions for which the mobile device screen remained locked throughout from those sessions when the screen was unlocked. Their work indicates that, on average, users interact with their devices for a total of 117 minutes a day, separated over 57 interaction sessions. Surprisingly, users unlocked their device for only 43% of the sessions. The full details of their work and results are available in their workshop paper.
Programming Competition winners Daniel Hintze, Rainhard D. Findling, Muhammad Muaaz, Sebastian Scholz and René Mayrhofer