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UBICOMP Tables of Contents: 01020304050607080910111213-113-214-114-215

Proceedings of the 2013 International Joint Conference on Pervasive and Ubiquitous Computing

Fullname:Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Editors:Friedemann Mattern; Silvia Santini; John F. Canny; Marc Langheinrich; Jun Rekimoto
Location:Zurich, Switzerland
Dates:2013-Sep-08 to 2013-Sep-12
Standard No:ISBN: 978-1-4503-1770-2; ACM DL: Table of Contents; hcibib: UBICOMP13-1
Links:Conference Website
  1. UBICOMP 2013-09-08 Volume 1
    1. Joint UbiComp/ISWC keynote 1
    2. Croudsourcing I
    3. Context sensing
    4. At work
    5. Home heating
    6. Health I
    7. Location-based services I
    8. Crowdsourcing II
    9. Emotion and behavior I
    10. Authentication
    11. Sport and fitness
    12. Systems
    13. Sustainability I
    14. Positioning I
    15. Activity recognition
    16. Hardware
    17. Domestic computing
    18. Novel interfaces
    19. Mobility
    20. Location-based services II
    21. User experience design
    22. Location privacy
    23. Mobile devices
    24. Health II
    25. Computing in the home
    26. Social computing I
    27. Sustainability II
    28. Emotion and behavior II
    29. Social computing II
    30. Food and nutrition
    31. Public displays
    32. Positioning II
    33. Education

UBICOMP 2013-09-08 Volume 1

Joint UbiComp/ISWC keynote 1

Creating the magic with information technology BIBAFull-Text 1-2
  Markus Gross
Advanced information technology has become a key enabler in modern media and entertainment. This comprises the production of animation or live action films, the design of next-generation toys and consumer products, or the creation of richer experiences in theme parks. At Disney Research Zurich, more than 200 researchers and scientists are working at the forefront of innovation in entertainment technology. Our research covers a wide spectrum of different fields, including graphics and animation, human computer interaction, wireless communication, computer vision, materials and design, robotics, and more. In this talk I will demonstrate how innovations in information technology and computational methods developed at Disney Research are serving as platforms for future content creation. I will emphasize the transformative power of 3D printing, digital fabrication, and our increasing ability to make the whole world responsive and interactive.

Croudsourcing I

Understanding the coverage and scalability of place-centric crowdsensing BIBAFull-Text 3-12
  Yohan Chon; Nicholas D. Lane; Yunjong Kim; Feng Zhao; Hojung Cha
Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.
Sensing the pulse of urban refueling behavior BIBAFull-Text 13-22
  Fuzheng Zhang; David Wilkie; Yu Zheng; Xing Xie
Urban transportation is increasingly studied due to its complexity and economic importance. It is also a major component of urban energy use and pollution. The importance of this topic will only increase as urbanization continues around the world. A less researched aspect of transportation is the refueling behavior of drivers. In this paper, we propose a step toward real-time sensing of refueling behavior and citywide petrol consumption. We use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits, measure the time spent, and estimate overall demand. For times and stations with sparse data, we use collaborative filtering to estimate conditions. Our system provides real-time estimates of gas stations' waiting times, from which recommendations could be made, an indicator of overall gas usage, from which macro-scale economic decisions could be made, and a geographic view of the efficiency of gas station placement.
If you see something, swipe towards it: crowdsourced event localization using smartphones BIBAFull-Text 23-32
  Robin Wentao Ouyang; Animesh Srivastava; Prithvi Prabahar; Romit Roy Choudhury; Merideth Addicott; F. Joseph McClernon
This paper presents iSee, a crowdsourced approach to detecting and localizing events in outdoor environments. Upon spotting an event, an iSee user only needs to swipe on her smartphone's touchscreen in the direction of the event. These swiping directions are often inaccurate and so are the compass measurements. Moreover, the swipes do not encode any notion of how far the event is located from the user, neither is the GPS location of the user accurate. Furthermore, multiple events may occur simultaneously and users do not explicitly indicate which events they are swiping towards. Nonetheless, as more users start contributing data, we show that our proposed system is able to quickly detect and estimate the locations of the events. We have implemented iSee on Android phones and have experimented in real-world settings by planting virtual "events" in our campus and asking volunteers to swipe on seeing one. Results show that iSee performs appreciably better than established triangulation and clustering-based approaches, in terms of localization accuracy, detection coverage, and robustness to sensor noise.

Context sensing

Headio: zero-configured heading acquisition for indoor mobile devices through multimodal context sensing BIBAFull-Text 33-42
  Zheng Sun; Shijia Pan; Yu-Chi Su; Pei Zhang
Heading information becomes widely used in ubiquitous computing applications for mobile devices. Digital magnetometers, also known as geomagnetic field sensors, provide absolute device headings relative to the earth's magnetic north. However, magnetometer readings are prone to significant errors in indoor environments due to the existence of magnetic interferences, such as from printers, walls, or metallic shelves. These errors adversely affect the performance and quality of user experience of the applications requiring device headings. In this paper, we propose Headio, a novel approach to provide reliable device headings in indoor environments. Headio achieves this by aggregating ceiling images of an indoor environment, and by using computer vision-based pattern detection techniques to provide directional references. To achieve zero-configured and energy-efficient heading sensing, Headio also utilizes multimodal sensing techniques to dynamically schedule sensing tasks. To fully evaluate the system, we implemented Headio on both Android and iOS mobile platforms, and performed comprehensive experiments in both small-scale controlled and large-scale public indoor environments. Evaluation results show that Headio constantly provides accurate heading detection performance in diverse situations, achieving better than 1 degree average heading accuracy, up to 33X improvement over existing techniques.
Crowd++: unsupervised speaker count with smartphones BIBAFull-Text 43-52
  Chenren Xu; Sugang Li; Gang Liu; Yanyong Zhang; Emiliano Miluzzo; Yih-Farn Chen; Jun Li; Bernhard Firner
Smartphones are excellent mobile sensing platforms, with the microphone in particular being exercised in several audio inference applications. We take smartphone audio inference a step further and demonstrate for the first time that it's possible to accurately estimate the number of people talking in a certain place -- with an average error distance of 1.5 speakers -- through unsupervised machine learning analysis on audio segments captured by the smartphones. Inference occurs transparently to the user and no human intervention is needed to derive the classification model. Our results are based on the design, implementation, and evaluation of a system called Crowd++, involving 120 participants in 10 very different environments. We show that no dedicated external hardware or cumbersome supervised learning approaches are needed but only off-the-shelf smartphones used in a transparent manner. We believe our findings have profound implications in many research fields, including social sensing and personal wellbeing assessment.
Lumina: a soft kinetic material for morphing architectural skins and organic user interfaces BIBAFull-Text 53-62
  Chin Koi Khoo; Flora D. Salim
The pervasive computing era has seen sensor and actuator technologies integrated into the design of kinetic building skins. This paper presents an investigation of a new soft kinetic material that has potential applications for morphing architectural building skins and organic user interfaces. The material capacities of Lumina to sense the ambient environment, morph and change forms, and emit light are demonstrated in the two prototypes presented in the paper. The first prototype is Blind, a form-changing organic user interface with multiple eye-like apertures that can be programmed to accept data input for visual communication. The second prototype is Blanket, a responsive morphing architectural skin with minimal mechanical and discrete components that sense real-time space occupancy data, manipulate light effects, perform active illumination, and act as an ambient display.

At work

A field study of multi-device workflows in distributed workspaces BIBAFull-Text 63-72
  Stephanie Santosa; Daniel Wigdor
With the selection of devices encompassing a wider range of computing surfaces, along with the near ubiquity of wireless networks, the nature of the workspace has become distributed over multiple locations and digital artifacts. We interviewed 22 professionals across a wide range of industries about their use of artifacts in their workflows to dis-cover new cross-device interaction paradigms and issues. We explore the impact of today's cloud services and app-based computing on how devices are used together. Gaps in data management and cross-device interactions were identified as the main obstacles and opportunities for improvement for multi-device interaction.
The break-time barometer: an exploratory system forworkplace break-time social awareness BIBAFull-Text 73-82
  Reuben Kirkham; Sebastian Mellor; David Green; Jiun-Shian Lin; Karim Ladha; Cassim Ladha; Daniel Jackson; Patrick Olivier; Peter Wright; Thomas Ploetz
The Break-Time Barometer is a social awareness system, which was developed as part of an exploratory study of the use of situated sensing and displays to promote cohesion in a newly-dispersed workplace. The Break-Time Barometer specifically aims to use an ambient persuasion approach in order to encourage people to join existing breaks, which take place within this community. Drawing upon a privacy-sensitive ubiquitous sensing infrastructure, the system of-fers information about potentially break-related activity in social spaces within this workplace, including alerts when specific events are detected. The system was developed using a user-centered iterative design approach. A qualitative mixed methods evaluation of a full deployment identified a diverse set of reactions to both the system and the design goal, and further elaborated the challenges of designing for social connectedness in this complex workplace context.
CoenoFire: monitoring performance indicators of firefighters in real-world missions using smartphones BIBAFull-Text 83-92
  Sebastian Feese; Bert Arnrich; Gerhard Troster; Michael Burtscher; Bertolt Meyer; Klaus Jonas
Firefighting is a dangerous task and many research projects have aimed at supporting firefighters during missions by developing new and often costly equipment. In contrast to previous approaches, we use the smartphone to monitor firefighters during real-world missions in order to provide objective data that can be used in post-incident briefings and trainings. In this paper, we present CoenoFire, a smartphone based sensing system aimed at monitoring temporal and behavioral performance indicators of firefighting missions. We validate the performance metrics showing that they can indicate why certain teams performed faster than others in a training scenario conducted by 16 firefighting teams. Furthermore, we deployed CoenoFire over a period of six weeks in a professional fire brigade. In total, 71 firefighters participated in our study and the collected data includes 76 real-world missions totaling to over 148 hours of mission data. Additionally, we visualize real-world mission data and show how mission feedback is supported by the data.

Home heating

Learning from a learning thermostat: lessons for intelligent systems for the home BIBAFull-Text 93-102
  Rayoung Yang; Mark W. Newman
Everyday systems and devices in the home are becoming smarter. In order to better understand the challenges of deploying an intelligent system in the home, we studied the experience of living with an advanced thermostat, the Nest. The Nest utilizes machine learning, sensing, and networking technology, as well as eco-feedback features. We conducted interviews with 23 participants, ten of whom also participated in a three-week diary study. Our findings show that while the Nest was well-received overall, the intelligent features of the Nest were not perceived to be as useful or intuitive as expected, in particular due to the system's inability to understand the intent behind sensed behavior and users' difficulty in understanding how the Nest works. A number of participants developed workarounds for the shortcomings they encountered. Based on our observations, we propose three avenues for future development of interactive intelligent technologies for the home: exception flagging, incidental intelligibility, and constrained engagement.
TherML: occupancy prediction for thermostat control BIBAFull-Text 103-112
  Christian Koehler; Brian D. Ziebart; Jennifer Mankoff; Anind K. Dey
Reducing the large energy consumption of temperature regulation systems is a challenge for researchers and practitioners alike. In this paper, we explore and compare two common types of solutions: A manual systems that encourages reduced energy use, and an intelligent automatic control system. We deployed an eco-feedback system with the ability to remotely control one's thermostat to ten participants for three months. Participants appreciated the ability to remotely control the thermostat, and controlled their heating system with 78.8% accuracy, a 6.3% improvement over not having this system. However, despite having feedback and remote control, they still wasted a lot of energy heating when away from home for the day. Using data from our deployment, we developed TherML, an occupancy prediction algorithm that uses GPS data from a user's smartphone to automatically control the indoor temperature of a home with 92.1% accuracy. We compare TherML to other state-of-the-art techniques, and show that the higher accuracy of our approach optimizes both energy usage and user comfort. We end with recommendations for a mixed initiative system that leverages aspects of both the manual and automated approaches that can better match heating control to users' routines and preferences.
Understanding adaptive thermal comfort: new directions for UbiComp BIBAFull-Text 113-122
  Adrian K. Clear; Janine Morley; Mike Hazas; Adrian Friday; Oliver Bates
In many parts of the world, mechanical heating and cooling is used to regulate indoor climates, with the aim of maintaining a uniform temperature. Achieving this is energy-intensive, since large indoor spaces must be constantly heated or cooled, and the difference to the outdoor temperature is large. This paper starts from the premise that comfort is not delivered to us by the indoor environment, but is instead something that is pursued as a normal part of daily life, through a variety of means. Based on a detailed study of four university students over several months, we explore how Ubicomp technologies can help create a more sustainable reality where people are more active in pursuing and maintaining their thermal comfort, and environments are less tightly controlled and less energy-intensive, and we outline areas for future research in this domain.

Health I

Detecting cocaine use with wearable electrocardiogram sensors BIBAFull-Text 123-132
  Annamalai Natarajan; Abhinav Parate; Edward Gaiser; Gustavo Angarita; Robert Malison; Benjamin Marlin; Deepak Ganesan
Ubiquitous physiological sensing has the potential to profoundly improve our understanding of human behavior, leading to more targeted treatments for a variety of disorders. The long term goal of this work is development of novel computational tools to support the study of addiction in the context of cocaine use. The current paper takes the first step in this important direction by posing a simple, but crucial question: Can cocaine use be reliably detected using wearable electrocardiogram (ECG) sensors? The main contributions in this paper include the presentation of a novel clinical study of cocaine use, the development of a computational pipeline for inferring morphological features from noisy ECG waveforms, and the evaluation of feature sets for cocaine use detection. Our results show that 32mg/70kg doses of cocaine can be detected with the area under the receiver operating characteristic curve levels above 0.9 both within and between-subjects.
Supporting disease insight through data analysis: refinements of the monarca self-assessment system BIBAFull-Text 133-142
  Mads Frost; Afsaneh Doryab; Maria Faurholt-Jepsen; Lars Vedel Kessing; Jakob E. Bardram
There is a growing interest in personal health technologies that sample behavioral data from a patient and visualize this data back to the patient for increased health awareness. However, a core challenge for patients is often to understand the connection between specific behaviors and health, i.e. to go beyond health awareness to disease insight. This paper presents MONARCA 2.0, which records subjective and objective data from patients suffering from bipolar disorder, processes this, and informs both the patient and clinicians on the importance of the different data items according to the patient's mood. The goal is to provide patients with a increased insight into the parameters influencing the nature of their disease. The paper describes the user-centered design and the technical implementation of the system, as well as findings from an initial field deployment.
A wearable projector-based gait assistance system and its application for elderly people BIBAFull-Text 143-152
  Satoshi Murata; Masanori Suzuki; Kaori Fujinami
The ability to walk is particularly important to maintain a person's quality of life (QOL). In today's aged society, ways to support the impaired gait of elderly people with a decline in physical function is in great demand. This paper proposes wearable projector-based gait assistance as a novel application of mobile projectors. The technical challenge is to compensate the projected image with the intended position and size during walking. To verify the concept, we developed a self-gait training assistance system that displays stride length information on the floor while the user is walking. We conducted a study with ten healthy older adults (ages: 76-91). The results show the effectiveness of visual clues in controlling stride length and elderly people's acceptance of the wearable projector device.

Location-based services I

FYI: communication style preferences underlie differences in location-sharing adoption and usage BIBAFull-Text 153-162
  Xinru Page; Bart P. Knijnenburg; Alfred Kobsa
In a mixed-methods study on adoption of location-sharing social networks (LSSN), we discovered that variations in adoption and usage behavior could be explained by one's predisposition to communicate in a certain style. Specifically, we found that certain individuals prefer a communication style we call FYI (For Your Information). FYI communicators like to infer availability and to keep in touch with others without having to interact with them, which is the predominant style in current LSSN. Using structural equation modeling on a U.S. nationwide survey (N=1021), we show how the FYI communication style predicts the adoption of LSSN while also showing a negative effect on one's desire to call someone on the phone. Moreover, we find that as age increases, FYI preference significantly decreases. In a follow-on survey (N=180), we refine the FYI construct and show that it affects users' level of disclosure and participation in social media. Furthermore, we show that it completely mediates the effect of certain Big-5 personality traits on social media participation and LSSN usage. The results suggest that to cater to a wider segment of the population, LSSN (and arguably any social media) should support an active communication style.
Placer: semantic place labels from diary data BIBAFull-Text 163-172
  John Krumm; Dany Rouhana
Semantic place labels are labels like "home", "work", and "school" given to geographic locations where a person spends time. Such labels are important both for giving understandable location information to people and for automatically inferring activities. Deployed products often compute semantic labels with heuristics, which are difficult to program reliably. In this paper, we develop Placer, an algorithm to infer semantic places labels. It uses data from two large, government diary studies to create a principled algorithm for labeling places based on machine learning. Our labeling reduces to a classification problem, where we classify locations into different label categories based on individual demographics, the timing of visits, and nearby businesses. Using these government studies gives us an unprecedented amount of training and test data. For instance, one of our experiments used training data from 87,600 place visits (from 10,372 distinct people) evaluated on 1,135,053 visits (from 124,517 distinct people). We show labeling accuracy for a number of experiments, including one that gives a 14 percentage point increase in accuracy when labeling is a function of nearby businesses in addition to demographic and time features. We also test on GPS data from 28 subjects.
Experiences with a social travel information system BIBAFull-Text 173-182
  Mike Harding; Joseph Finney; Nigel Davies; Mark Rouncefield; James Hannon
This paper documents a programme of research to explore the development of mobile social travel information systems, where dynamic travel information is produced by travellers themselves and distributed within communities united by similar travel patterns and everyday activities. The resulting system, called OurTravel, was the subject of a series of real-world trials involving three diverse physical communities: a rural village, a group of urban office workers and the attendees of a contemporary arts festival. We describe the design and implementation of the OurTravel system, our experiences of running these trials and the insights gained.

Crowdsourcing II

Contextual dissonance: design bias in sensor-based experience sampling methods BIBAFull-Text 183-192
  Neal Lathia; Kiran K. Rachuri; Cecilia Mascolo; Peter J. Rentfrow
The Experience Sampling Method (ESM) has been widely used to collect longitudinal survey data from participants; in this domain, smartphone sensors are now used to augment the context-awareness of sampling strategies. In this paper, we study the effect of ESM design choices on the inferences that can be made from participants' sensor data, and on the variance in survey responses that can be collected. In particular, we answer the question: are the behavioural inferences that a researcher makes with a trigger-defined subsample of sensor data biased by the sampling strategy's design? We demonstrate that different single-sensor sampling strategies will result in what we refer to as contextual dissonance: a disagreement in how much different behaviours are represented in the aggregated sensor data. These results are not only relevant to researchers who use the ESM, but call for future work into strategies that may alleviate the biases that we measure.
SOUK: social observation of human kinetics BIBAFull-Text 193-196
  Marc-Olivier Killijian; Matthieu Roy; Gilles Trédan; Christophe Zanon
Simulating human-centered pervasive systems requires accurate assumptions on the behavior of human groups. Recent models consider this behavior as a combination of both social and spatial factors. Yet, establishing accurate traces of human groups is difficult: current techniques capture either positions, or contacts, with a limited accuracy.
   In this paper we introduce a new technique to capture such behaviors. The interest of this approach lies in the unprecedented accuracy at which both positions and orientations of humans, even gathered in a crowd, are captured.
   From the mobility to the topological connectivity, the open-source framework we developed offers a layered approach that can be tailored, allowing to compare and reason about models and traces.
   We introduce a new trace of 50 individuals on which the validity and accuracy of this approach is demonstrated. To showcase the interest of our software pipeline, we compare it against the random waypoint model. Our fine-grained analyses, that take into account social interactions between users, show that the random waypoint model is not a reasonable approximation of any of the phenomena we observed.

Emotion and behavior I

Your reactions suggest you liked the movie: automatic content rating via reaction sensing BIBAFull-Text 197-206
  Xuan Bao; Songchun Fan; Alexander Varshavsky; Kevin Li; Romit Roy Choudhury
This paper describes a system for automatically rating content -- mainly movies and videos -- at multiple granularities. Our key observation is that the rich set of sensors available on today's smartphones and tablets could be used to capture a wide spectrum of user reactions while users are watching movies on these devices. Examples range from acoustic signatures of laughter to detect which scenes were funny, to the stillness of the tablet indicating intense drama. Moreover, unlike in most conventional systems, these ratings need not result in just one numeric score, but could be expanded to capture the user's experience. We combine these ideas into an Android based prototype called Pulse, and test it with 11 users each of whom watched 4 to 6 movies on Samsung tablets. Encouraging results show consistent correlation between the user's actual ratings and those generated by the system. With more rigorous testing and optimization, Pulse could be a candidate for real-world adoption.
Classifying social actions with a single accelerometer BIBAFull-Text 207-210
  Hayley Hung; Gwenn Englebienne; Jeroen Kools
In this paper, we estimate different types of social actions from a single body-worn accelerometer in a crowded social setting. Accelerometers have many advantages in such settings: they are impervious to environmental noise, unobtrusive, cheap, low-powered, and their readings are specific to a single person. Our experiments show that they are surprisingly informative of different types of social actions. The social actions we address in this paper are whether a person is speaking, laughing, gesturing, drinking, or stepping. To our knowledge, this is the first work to carry out experiments on estimating social actions from conversational behavior using only a wearable accelerometer. The ability to estimate such actions using just the acceleration opens up the potential for analyzing more about social aspects of people's interactions without explicitly recording what they are saying.


Exploring capturable everyday memory for autobiographical authentication BIBAFull-Text 211-220
  Sauvik Das; Eiji Hayashi; Jason I. Hong
We explore how well the intersection between our own everyday memories and those captured by our smartphones can be used for what we call autobiographical authentication-a challenge-response authentication system that queries users about day-to-day experiences. Through three studies-two on MTurk and one field study-we found that users are good, but make systematic errors at answering autobiographical questions. Using Bayesian modeling to account for these systematic response errors, we derived a formula for computing a confidence rating that the attempting authenticator is the user from a sequence of question-answer responses. We tested our formula against five simulated adversaries based on plausible real-life counterparts. Our simulations indicate that our model of autobiographical authentication generally performs well in assigning high confidence estimates to the user and low confidence estimates to impersonating adversaries.
Using a 2DST waveguide for usable, physically constrained out-of-band Wi-Fi authentication BIBAFull-Text 221-224
  Matthias Budde; Marcel Köpke; Matthias Berning; Till Riedel; Michael Beigl
This paper proposes using a 2D waveguide for a novel means of authentication in public Wi-Fi infrastructures. The design of the system is presented, and its practicability and usability is comparatively discussed with that of five other tag and context based authentication schemes, two of which have not been previously realized. In accordance with the presented application scenarios, all of the schemes were implemented in a platform-independent fashion built on web technology.

Sport and fitness

Walk detection and step counting on unconstrained smartphones BIBAFull-Text 225-234
  Agata Brajdic; Robert Harle
Smartphone pedometry offers the possibility of ubiquitous health monitoring, context awareness and indoor location tracking through Pedestrian Dead Reckoning (PDR) systems. However, there is currently no detailed understanding of how well pedometry works when applied to smartphones in typical, unconstrained use.
   This paper evaluates common walk detection (WD) and step counting (SC) algorithms applied to smartphone sensor data. Using a large dataset (27 people, 130 walks, 6 smartphone placements) optimal algorithm parameters are provided and applied to the data. The results favour the use of standard deviation thresholding (WD) and windowed peak detection (SC) with error rates of less than 3%. Of the six different placements, only the back trouser pocket is found to degrade the step counting performance significantly, resulting in undercounting for many algorithms.
ClimbAX: skill assessment for climbing enthusiasts BIBAFull-Text 235-244
  Cassim Ladha; Nils Y. Hammerla; Patrick Olivier; Thomas Plötz
In recent years the sport of climbing has seen consistent increase in popularity. Climbing requires a complex skill set for successful and safe exercising. While elite climbers receive intensive expert coaching to refine this skill set, this progression approach is not viable for the amateur population. We have developed ClimbAX -- a climbing performance analysis system that aims for replicating expert assessments and thus represents a first step towards an automatic coaching system for climbing enthusiasts. Through an accelerometer based wearable sensing platform, climber's movements are captured. An automatic analysis procedure detects climbing sessions and moves, which form the basis for subsequent performance assessment. The assessment parameters are derived from sports science literature and include: power, control, stability, speed. ClimbAX was evaluated in a large case study with 53 climbers under competition settings. We report a strong correlation between predicted scores and official competition results, which demonstrate the effectiveness of our automatic skill assessment system.
Estimating heart rate variation during walking with smartphone BIBAFull-Text 245-254
  Mayu Sumida; Teruhiro Mizumoto; Keiichi Yasumoto
Aiming to realize the application which supports users to enjoy walking with an appropriate physical load, we propose a method to estimate physical load and its variation during walking only with available functions of a smartphone. Since physical load has a linear relationship with heart rate, our purpose is to estimate heart rate with a smartphone. To this end, we build heart rate prediction models which predict heart rate variation from walking data including acceleration and walking speed by machine learning. In order to track unexpected change of physical load, we focus attention on oxygen uptake which has a similar property to heart rate and devise a novel technique to estimate the oxygen uptake from acceleration and GPS data so that it is used as an input of the model. Moreover, to adapt to difference of heart rate variation among individuals, we devise techniques to optimize parameters for each profile-based category of users and to normalize heart rate to absorb individual difference. We applied the proposed method to actual walking data on various routes by different persons and confirmed that the method estimates heart rate variation with the mean error of less than 7 beat per minute.


A cloud-powered driver-less printing system for smartphones BIBAFull-Text 255-264
  Seungeun Chung; Shuiqing Wang; Injong Rhee
Smart devices such as smartphones and tablets are becoming more powerful and versatile enough to replace conventional personal computers. Despite the rapid evolution in their capabilities, controlling peripherals such as network printers directly from smart devices is still in the primitive stage due to the lack of dedicated drivers. We propose and prototype a cloud-powered, driver-less printing system called CloudBridge for ubiquitous printing support from off the shelf smart devices. The CloudBridge service, which runs on a smart device, operates as a communication bridge connecting a network printer and a cloud server. By using cloud's ability to translate the operation commands into a language that the printer can understand, it is possible for a smart device to control the printer without having dedicated drivers. CloudBridge achieves the true meaning of ubiquitous mobile printing: it does not require any prerequisite settings. Compared to most widely used mobile printing solutions, the operation time is reasonable, compensating for the time and effort required for setting up the solution. CloudBridge is further optimized to improve the quality of experience, such as response time and energy consumption of the smart device, by adopting an adaptive compression method.
CacheKeeper: a system-wide web caching service for smartphones BIBAFull-Text 265-274
  Yifan Zhang; Chiu Tan; Li Qun
Efficient web caching in mobile apps eliminates unnecessary network traffic, reduces web accessing latency, and improves smartphone battery life. However, recent research has indicated that current mobile apps suffer from poor implementations of web caching. In this work, we first conducted a comprehensive survey of over 1000 Android apps to identify how different types of mobile apps perform in web caching. Based on our analysis, we designed CacheKeeper, an OS web caching service transparent to mobile apps for smartphones. CacheKeeper can not only effectively reduce overhead caused by poor web caching of mobile apps, but also utilizes cross-app caching opportunities in smartphones. Furthermore, CacheKeeper is backward compatible, meaning that existing apps can take advantage of CacheKeeper without any modifications. We have implemented a prototype of CacheKeeper in Linux kernel. Evaluation on 10 top ranked Android apps shows that our CacheKeeper prototype can save 42% networks traffic with real user browsing behaviors and increase web accessing speed by 2x under real 3G settings. Experiments also show that our prototype incurs negligible overhead in most aspects on cache misses.
Practical prediction and prefetch for faster access to applications on mobile phones BIBAFull-Text 275-284
  Abhinav Parate; Matthias Böhmer; David Chu; Deepak Ganesan; Benjamin M. Marlin
Mobile phones have evolved from communication devices to indispensable accessories with access to real-time content. The increasing reliance on dynamic content comes at the cost of increased latency to pull the content from the Internet before the user can start using it. While prior work has explored parts of this problem, they ignore the bandwidth costs of prefetching, incur significant training overhead, need several sensors to be turned on, and do not consider practical systems issues that arise from the limited background processing capability supported by mobile operating systems. In this paper, we make app prefetch practical on mobile phones. Our contributions are two-fold. First, we design an app prediction algorithm, APPM, that requires no prior training, adapts to usage dynamics, predicts not only which app will be used next but also when it will be used, and provides high accuracy without requiring additional sensor context. Second, we perform parallel prefetch on screen unlock, a mechanism that leverages the benefits of prediction while operating within the constraints of mobile operating systems. Our experiments are conducted on long-term traces, live deployments on the Android Play Market, and user studies, and show that we outperform prior approaches to predicting app usage, while also providing practical ways to prefetch application content on mobile phones.

Sustainability I

The timestreams platform: artist mediated participatory sensing for environmental discourse BIBAFull-Text 285-294
  Jesse Blum; Martin Flintham; Rachel Jacobs; Victoria Shipp; Genovefa Kefalidou; Michael Brown; Derek McAuley
Ubiquitous and pervasive computing techniques have been used to inform discourses around climate change and energy insecurity, traditionally through data capture and representation for scientists, policy makers and the public. Research into re-engaging the public with sustainability and climate change issues reveals the significance of emotional and personal engagement alongside locally meaningful, globally-relevant and data-informed climate messaging for the public. New ubiquitous and pervasive computing techniques are emerging to support the next generation of climate change stakeholders, including artists, community practitioners, educators and data hackers, to create scientific data responsive artworks and performances. Grounded in our experiences of community based artistic interventions, we explore the design and deployments of the Timestreams platform, demonstrating usages of ubiquitous and pervasive computing within these new forms of discourse around climate change and energy insecurity.
The collective infrastructural work of electricity: exploring feedback in a prepaid university dorm in China BIBAFull-Text 295-304
  Tengfei Liu; Xianghua Ding; Silvia Lindtner; Tun Lu; Ning Gu
Feedback on resource consumption is often explored as a way to raise awareness and saving resources. This paper reports findings from a user study of a feedback system deployed in a Chinese university dormitory with a prepaid electricity system, a context different from the more common domestic setting in the West explored in prior research. With this work, we move beyond resource conservation and draw attention to an often-neglected aspect of infrastructural work -- the work to ensure the smooth and continuous supply of resources from end users. This paper examines the ways in which people attend to electricity through what we term collective infrastructural work, i.e. people perceive electricity as a marginal concern, and yet invest time to maintain it collectively. We draw out a number of implications for design and evaluation from this work.
Exploring sustainability research in computing: where we are and where we go next BIBAFull-Text 305-314
  Bran Knowles; Lynne Blair; Mike Hazas; Stuart Walker
This paper develops a holistic framework of questions which seem to motivate sustainability research in computing in order to enable new opportunities for critique. Analysis of systematically selected corpora of computing publications demonstrates that several of these question areas are well covered, while others are ripe for further exploration. It also provides insight into which of these questions tend to be addressed by different communities within sustainable computing. The framework itself reveals discursive similarities between other existing environmental discourses, enabling reflection and participation with the broader sustainability debate. It is argued that the current computing discourse on sustainability is reformist and premised in a Triple Bottom Line construction of sustainability. A radical, Quadruple Bottom Line alternative is explored as a new vista for computing research.

Positioning I

Hallway based automatic indoor floorplan construction using room fingerprints BIBAFull-Text 315-324
  Yifei Jiang; Yun Xiang; Xin Pan; Kun Li; Qin Lv; Robert P. Dick; Li Shang; Michael Hannigan
People spend approximately 70% of their time indoors. Understanding the indoor environments is therefore important for a wide range of emerging mobile personal and social applications. Knowledge of indoor floorplans is often required by these applications. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor.
   This paper describes an automatic indoor floorplan construction system. Leveraging Wi-Fi fingerprints and user motion information, this system automatically constructs floorplan via three key steps: (1) room adjacency graph construction to determine which rooms are adjacent; (2) hallway layout learning to estimate room sizes and order rooms along each hallway, and (3) force directed dilation to adjust room sizes and optimize the overall floorplan accuracy. Deployment study in three buildings with 189 rooms demonstrates high floorplan accuracy. The system has been implemented as a mobile middleware, which allows emerging mobile applications to generate, leverage, and share indoor floorplans.
Find my stuff: supporting physical objects search with relative positioning BIBAFull-Text 325-334
  Jens Nickels; Pascal Knierim; Bastian Könings; Florian Schaub; Björn Wiedersheim; Steffen Musiol; Michael Weber
Searching for misplaced keys, phones, or wallets is a common nuisance. Find My Stuff (FiMS) provides search support for physical objects inside furniture, on room level, and in multiple locations, e.g., home and office. Stuff tags make objects searchable while all other localization components are integrated into furniture. FiMS requires minimal configuration and automatically adapts to the user's furniture arrangement. Object search is supported with relative position cues, such as "phone is inside top drawer" or "the wallet is between couch and table," which do not require exact object localization. Functional evaluation of our prototype shows the approach's practicality with sufficient accuracy in realistic environments and low energy consumption. We also conducted two user studies, which showed that objects can be retrieved significantly faster with FiMS than manual search and that our relative position cues provide better support than map-based cues. Combined with audiovisual feedback, FiMS also outperforms spotlight-based cues.

Activity recognition

Automatically detecting problematic use of smartphones BIBAFull-Text 335-344
  Choonsung Shin; Anind K. Dey
Smartphone adoption has increased significantly and, with the increase in smartphone capabilities, this means that users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, there is growing evidence of problematic use of smartphones that impacts both social and heath aspects of users' lives. Currently, assessment of overuse or problematic use depends on one-time, self-reported behavioral information about phone use. Due to the known issues with self-reports in such types of assessments, we explore an automated, objective and repeatable approach for assessing problematic usage. We collect a wide range of phone usage data from smartphones, identify a number of usage features that are relevant to this assessment, and build detection models based on Adaboost with machine learning algorithms automatically detecting problematic use. We found that the number of apps used per day, the ratio of SMSs to calls, the number of event-initiated sessions, the number of apps used per event initiated session, and the length of non-event-initiated sessions are useful for detecting problematic usage. With these, a detection model can identify users with problematic usage with 89.6% accuracy (F-score of .707).
A probabilistic ontological framework for the recognition of multilevel human activities BIBAFull-Text 345-354
  Rim Helaoui; Daniele Riboni; Heiner Stuckenschmidt
A major challenge of ubiquitous computing resides in the acquisition and modelling of rich and heterogeneous context data, among which, ongoing human activities at different degrees of granularity. In a previous work, we advocated the use of probabilistic description logics (DLs) in a multilevel activity recognition framework. In this paper, we present an in-depth study of activity modeling and reasoning within that framework, as well as an experimental evaluation with a large real-world dataset. Our solution allows us to cope with the uncertain nature of ontological descriptions of activities, while exploiting the expressive power and inference tools of the OWL 2 language. Targeting a large dataset of real human activities, we developed a probabilistic ontology modeling nearly 150 activities and actions of daily living. Experiments with a prototype implementation of our framework confirm the viability of our solution.
Towards zero-shot learning for human activity recognition using semantic attribute sequence model BIBAFull-Text 355-358
  Heng-Tze Cheng; Martin Griss; Paul Davis; Jianguo Li; Di You
Understanding human activities is important for user-centric and context-aware applications. Previous studies showed promising results using various machine learning algorithms. However, most existing methods can only recognize the activities that were previously seen in the training data. In this paper, we present a new zero-shot learning framework for human activity recognition that can recognize an unseen new activity even when there are no training samples of that activity in the dataset. We propose a semantic attribute sequence model that takes into account both the hierarchical and sequential nature of activity data. Evaluation on datasets in two activity domains show that the proposed zero-shot learning approach achieves 70-75% precision and recall recognizing unseen new activities, and outperforms supervised learning with limited labeled data for the new classes.
Ensembles of multiple sensors for human energy expenditure estimation BIBAFull-Text 359-362
  Hristijan Gjoreski; Boštjan Kaluza; Matjaz Gams; Radoje Milic; Mitja Luštrek
Monitoring human energy expenditure is important in many health and sport applications, since the energy expenditure directly reflects the level of physical activity. The actual energy expenditure is unpractical to measure; hence, the field aims at estimating it by measuring the physical activity with accelerometers and other sensors. Current advanced estimators use a context-dependent approach in which a different regression model is invoked for different activities of the user. In this paper, we go a step further and use multiple contexts corresponding to multiple sensors, resulting in an ensemble of models for energy expenditure estimation. This provides a multi-view perspective, which leads to a better estimation of the energy. The proposed method was experimentally evaluated on a comprehensive set of activities where it outperformed the current state-of-the-art.


Instant inkjet circuits: lab-based inkjet printing to support rapid prototyping of UbiComp devices BIBAFull-Text 363-372
  Yoshihiro Kawahara; Steve Hodges; Benjamin S. Cook; Cheng Zhang; Gregory D. Abowd
This paper introduces a low cost, fast and accessible technology to support the rapid prototyping of functional electronic devices. Central to this approach of 'instant inkjet circuits' is the ability to print highly conductive traces and patterns onto flexible substrates such as paper and plastic films cheaply and quickly. In addition to providing an alternative to breadboarding and conventional printed circuits, we demonstrate how this technique readily supports large area sensors and high frequency applications such as antennas. Unlike existing methods for printing conductive patterns, conductivity emerges within a few seconds without the need for special equipment. We demonstrate that this technique is feasible using commodity inkjet printers and commercially available ink, for an initial investment of around US$300. Having presented this exciting new technology, we explain the tools and techniques we have found useful for the first time. Our main research contribution is to characterize the performance of instant inkjet circuits and illustrate a range of possibilities that are enabled by way of several example applications which we have built. We believe that this technology will be of immediate appeal to researchers in the ubiquitous computing domain, since it supports the fabrication of a variety of functional electronic device prototypes.
Power harvesting from microwave oven electromagnetic leakage BIBAFull-Text 373-382
  Yoshihiro Kawahara; Xiaoying Bian; Ryo Shigeta; Rushi Vyas; Manos M. Tentzeris; Tohru Asami
In this paper, we considered the possibility of using electricity harvested from the microwave field leaked from commercial microwave ovens. Our experimental results showed that the leakage received by a dipole antenna was about 0 dBm (1 mW) at a point 5 cm in front of the door. A rectenna consisting of a dipole antenna and charge pump can convert the leaked microwave energy into a DC current. When a microwave oven is operated for 2 min, 9.98 mJ of energy was harvested. We demonstrated that this energy is sufficient for powering a digital cooking timer to count down for 3 min and beep for 2.5 s. The operation of other kitchen devices was also demonstrated.
Wirelessly powered bistable display tags BIBAFull-Text 383-386
  Artem Dementyev; Jeremy Gummeson; Derek Thrasher; Aaron Parks; Deepak Ganesan; Joshua R. Smith; Alanson P. Sample
Paper displays have a number of attractive properties, in particular the ability to present visual information perpetually with no power source. However, they are not digitally updatable or re-usable. Bistable display materials, such as e-paper, promise to enable displays with the best properties of both paper and electronic displays. However, rewriting a pixelated bistable display requires substantial energy, both for communication and for setting the pixel states.
   This paper describes a bistable display tag that, from an energy standpoint, is capable of perpetual operation. A commercial off-the-shelf NFC-enabled phone generates RF signals carrying both the information and energy necessary to update the display. After the update is complete, the display continues to present the information with no further power input. We present one example implementation, a companion display for a mobile phone that can be used to capture and preserve a screenshot. We also discuss other potential applications of energy neutral bistable display tags.
WebClip: a connector for ubiquitous physical input and output for touch screen devices BIBAFull-Text 387-390
  Thomas Kubitza; Norman Pohl; Tilman Dingler; Albrecht Schmidt
It has become extremely easy for developers to build custom software for smartphone and tablet computers. However, it is still hard to extend those devices with external electronics, e.g. additional sensors and actuators. In the moment when external hardware can be easily attached to phones and tablets a wide new application space will be opened up. With WebClip we present a device offering digital and analogue I/O ports that can be controlled and monitored by just clipping the device onto a capacitive touch screen. A web page in the browser of the touch screen device is used to control the bi-directional communication. Data from the WebClip to the device is sent by emulating touches on the screen whereas the reverse direction uses light sensors on the bottom side of the clip to receive light sequences emitted by the web page. A simple Javascript API is offered to build custom web applications. We have successfully tested our prototype with a variety of phones and tablet computers and report on performance and limitations.

Domestic computing

A tangible programming tool for creation of context-aware applications BIBAFull-Text 391-400
  Jisoo Lee; Luis Garduño; Erin Walker; Winslow Burleson
End-user programming tools, if properly designed, have the potential to empower end-users to create context-aware applications tailored to their own needs and lives, in order to help them break bad habits and change their behaviors. In this work, we present GALLAG Strip, an easy to use mobile and tangible tool that allows users to create context-aware applications without the need of programming experience. It enables programming by physical demonstration of envisioned interactions with the same sensors and objects that users will later encounter in their finished application. After an initial pilot to verify the usability of GALLAG Strip, we conducted a user study to evaluate the effects of tangible programming in terms of ease of use, engagement, and facilitation of the ideation process. We found that tangibility has both benefits and drawbacks, and suggest a mixed tangible and non-tangible approach for better user experience.
There's no such thing as gaining a pound: reconsidering the bathroom scale user interface BIBAFull-Text 401-410
  Matthew Kay; Dan Morris; mc schraefel; Julie A. Kientz
The weight scale is perhaps the most ubiquitous health sensor of all and is important to many health and lifestyle decisions, but its fundamental interface -- a single numerical estimate of a person's current weight -- has remained largely unchanged for 100 years. An opportunity exists to impact public health by re-considering this pervasive interface. Toward that end, we investigated the correspondence between consumers' perceptions of weight data and the realities of weight fluctuation. Through an analysis of online product reviews, a journaling study on weight fluctuations, expert interviews, and a large-scale survey of scale users, we found that consumers' perception of weight scale behavior is often disconnected from scales' capabilities and from clinical relevance, and that accurate understanding of weight fluctuation is associated with greater trust in the scale itself. We propose significant changes to how weight data should be presented and discuss broader implications for the design of other ubiquitous health sensing devices.
Detecting cooking state with gas sensors during dry cooking BIBAFull-Text 411-414
  Sen H. Hirano; Jed R. Brubaker; Donald J. Patterson; Gillian R. Hayes
Gas sensors have the potential to assist cooking by providing feedback on the cooking process and by further automating cooking. In this work, we explored the potential use of gas sensors to monitor food during the cooking process. Focusing on dry cooking, we collected gas emissions using 13 sensors during trials in which food was cooked to various degrees of doneness. Using decision tree classifiers, we were able to predict doneness for waffles and popcorn with 73% and 85% accuracy, respectively. We reflect on the potential reasons for this variation and the ways in which gas sensors might reliably be used in ubicomp applications to support cooking.
Dog's life: wearable activity recognition for dogs BIBAFull-Text 415-418
  Cassim Ladha; Nils Hammerla; Emma Hughes; Patrick Olivier; Thomas Ploetz
Health and well-being of dogs, either domesticated pets or service animals, are major concerns that are taken seriously for ethical, emotional, and financial reasons. Welfare assessments in dogs rely on objective observations of both frequency and variability of individual behaviour traits, which is often difficult to obtain in a dog's everyday life. In this paper we have identified a set of activities, which are linked to behaviour traits that are relevant for a dog's wellbeing. We developed a collar-worn accelerometry platform that records dog behaviours in naturalistic environments. A statistical classification framework is used for recognising dog activities. In an experimental evaluation we analysed the naturalistic behaviour of 18 dogs and were able to recognise a total of 17 different activities with approximately 70% classification accuracy. The presented system is the first of its kind that allows for robust and detailed analysis of dog activities in naturalistic environments.

Novel interfaces

AirWave: non-contact haptic feedback using air vortex rings BIBAFull-Text 419-428
  Sidhant Gupta; Dan Morris; Shwetak N. Patel; Desney Tan
Input modalities such as speech and gesture allow users to interact with computers without holding or touching a physical device, thus enabling at-a-distance interaction. It remains an open problem, however, to incorporate haptic feedback into such interaction. In this work, we explore the use of air vortex rings for this purpose. Unlike standard jets of air, which are turbulent and dissipate quickly, vortex rings can be focused to travel several meters and impart perceptible feedback. In this paper, we review vortex formation theory and explore specific design parameters that allow us to generate vortices capable of imparting haptic feedback. Applying this theory, we developed a prototype system called AirWave. We show through objective measurements that AirWave can achieve spatial resolution of less than 10 cm at a distance of 2.5 meters. We further demonstrate through a user study that this can be used to direct tactile stimuli to different regions of the human body.
NLify: lightweight spoken natural language interfaces via exhaustive paraphrasing BIBAFull-Text 429-438
  Seungyeop Han; Matthai Philipose; Yun-Cheng Ju
This paper presents the design and implementation of a programming system that enables third-party developers to add spoken natural language (SNL) interfaces to standalone mobile applications. The central challenge is to create statistical recognition models that are accurate and resource-efficient in the face of the variety of natural language, while requiring little specialized knowledge from developers. We show that given a few examples from the developer, it is possible to elicit comprehensive sets of paraphrases of the examples using internet crowds. The exhaustive nature of these paraphrases allows us to use relatively simple, automatically derived statistical models for speech and language understanding that perform well without per-application tuning. We have realized our design fully as an extension to the Visual Studio IDE. Based on a new benchmark dataset with 3500 spoken instances of 27 commands from 20 subjects and a small developer study, we establish the promise of our approach and the impact of various design choices.
Pursuits: spontaneous interaction with displays based on smooth pursuit eye movement and moving targets BIBAFull-Text 439-448
  Mélodie Vidal; Andreas Bulling; Hans Gellersen
Although gaze is an attractive modality for pervasive interactions, the real-world implementation of eye-based interfaces poses significant challenges, such as calibration. We present Pursuits, an innovative interaction technique that enables truly spontaneous interaction with eye-based interfaces. A user can simply walk up to the screen and readily interact with moving targets. Instead of being based on gaze location, Pursuits correlates eye pursuit movements with objects dynamically moving on the interface. We evaluate the influence of target speed, number and trajectory and develop guidelines for designing Pursuits-based interfaces. We then describe six realistic usage scenarios and implement three of them to evaluate the method in a usability study and a field study. Our results show that Pursuits is a versatile and robust technique and that users can interact with Pursuits-based interfaces without prior knowledge or preparation phase.


The influence of temporal and spatial features on the performance of next-place prediction algorithms BIBAFull-Text 449-458
  Paul Baumann; Wilhelm Kleiminger; Silvia Santini
Several algorithms to predict the next place visited by a user have been proposed in the literature. The accuracy of these algorithms -- measured as the ratio of the number of correct predictions and the number of all computed predictions -- is typically very high. In this paper, we show that this good performance is due to the high predictability intrinsic in human mobility. We also show that most algorithms fail to correctly predict transitions, i.e. situations in which users move between different places. To this end, we analyze the performance of 18 prediction algorithms focusing on their ability to predict transitions. We run our analysis on a data set of mobility traces of 37 users collected over a period of 1.5 years. Our results show that even algorithms achieving an overall high accuracy are unable to reliably predict the next location of the user if this is different from the current one. Building upon our analysis we then present a novel next-place prediction algorithm that can both achieve high overall accuracy and reliably predict transitions. Our approach combines all the 18 algorithms considered in our analysis and achieves its good performance at the cost of a higher computational and memory overhead.
Inferring human mobility patterns from taxicab location traces BIBAFull-Text 459-468
  Raghu Ganti; Mudhakar Srivatsa; Anand Ranganathan; Jiawei Han
Taxicabs equipped with real-time location sensing devices are increasingly becoming popular. Such location traces are a rich source of information and can be used for congestion pricing, taxicab placement, and improved city planning. An important problem to enable these application is to identify human mobility patterns from the taxicab traces, which translates to being able to identify pickup and dropoff points for a particular trip. In this paper, we show that while past approaches are effective in detecting hotspots using location traces, they are largely ineffective in identifying trips (pairs of pickup and dropoff points). We propose the use of a graph theory concept -- stretch factor in a novel manner to identify trip(s) made by a taxicab and show that a Hidden Markov Model based algorithm can identify trips (using real datasets from taxicab deployments in Shanghai and partially simulated datasets from Stockholm) with precision and recall of 90-94%, a significant improvement over past approaches that result in a precision and recall of about 50-60%.
Modelling heterogeneous location habits in human populations for location prediction under data sparsity BIBAFull-Text 469-478
  James McInerney; Jiangchuan Zheng; Alex Rogers; Nicholas R. Jennings
In recent years, researchers have sought to capture the daily life location behaviour of groups of people for exploratory, inference, and predictive purposes. However, development of such approaches has been limited by the requirement of personal semantic labels for locations or social/spatial overlap between individuals in the group. To address this shortcoming, we present a Bayesian model of mobility in populations (i.e., groups without spatial or social interconnections) that is not subject to any of these requirements. The model intelligently shares temporal parameters between people, but keeps the spatial parameters specific to individuals. To illustrate the advantages of population modelling, we apply our model to the difficult problem of overcoming data sparsity in location prediction systems, using the Nokia dataset comprising 38 individuals, and find a factor of 2.4 improvement in location prediction performance against a state-of-the-art model when training on only 20 hours of observations.

Location-based services II

Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs BIBAFull-Text 479-488
  Dingqi Yang; Daqing Zhang; Zhiyong Yu; Zhiwen Yu
The crowdsourced digital footprints from Location Based Social Networks (LBSNs) contain not only rich information about locations, but also individual's feeling about locations and associated entities. This new data source provides us with an unprecedented opportunity to massively and cheaply collect location related information, and to subtly characterize individual's fine-grained preference about those places and associated entities. In this paper, we propose SEALs -- a fine-grained preference-aware location search framework leveraging the crowdsourced traces in LBSNs. We first collect user check-ins and tips from Foursquare and use them as direct user feedback on locations. Second, we extract users' sentiment about locations and associated entities from tips to characterize their fine-grained location preference. Third, we incorporate such fine-grained user preference into personalized location ranking using tensor factorization techniques. Experimental results show that SEALs can achieve better location ranking comparing to the state-of-the-art solutions.
Fine-grained sharing of sensed physical activity: a value sensitive approach BIBAFull-Text 489-498
  Daniel A. Epstein; Alan Borning; James Fogarty
Personal informatics applications in a variety of domains are increasingly enabled by low cost personal sensing. Although applications capture fine-grained activity for self reflection, sharing is generally limited to high level summaries. There are potential advantages to fine-grained sharing, but also potential harms. To help investigate this complex design space, we employ Value Sensitive Design to consider whether and how to share fine grained step activity. We identify key values and value tensions, and we develop scenarios to highlight these. We then design a set of data transformations that seek to maximize the benefits while minimizing the harms of detailed sharing. These include a novel approach to interactive modification of fine grained step data, allowing people to remove private data and using motif discovery to generate realistic replacement data. Finally, we conduct semi structured interviews with 12 participants examining these scenarios and transformations. We distill results into a set of design considerations for fine-grained physical activity sharing.
A model for WLAN signal attenuation of the human body BIBAFull-Text 499-508
  Ngewi Fet; Marcus Handte; Pedro José Marrón
Fingerprinting-based indoor localization involves building a signal strength radio map. This map is usually built manually by a person holding the mapping device, which results in orientation-dependent fingerprints due to signal attenuation by the human body. To offset this distortion, fingerprints are typically collected for multiple orientations, but this requires a high effort for large localization areas. In this paper, we propose an approach to reduce the mapping effort by modeling the WLAN signal attenuation caused by the human body. By applying the model to the captured signal to compensate for the attenuation, it is possible to generate an orientation-independent fingerprint. We demonstrate that our model is location and person independent and its output is comparable with manually created radio maps. By using the model, the WLAN scanning effort can be reduced by 75% to 87.5% (depending on the number of orientations).

User experience design

Three case studies of UX with moving products BIBAFull-Text 509-518
  Jinyung Jung; Seok-Hyung Bae; Myung-Suk Kim
Advances in ubicomp technology are enabling the development of products that move in affective ways. However, there is insufficient empirical knowledge to encourage such designs. As research through design, we built three prototypes of standing-type kinetic products to conduct user experience (UX) field studies with visceral, behavioral, and reflective perspectives. Tasks, the users' body reactions, and their feelings were measured and interpreted to uncover features of a desirable UX with moving products. The findings and discussions contribute to the ubicomp community by expanding the design space for moving products and inspiring the community with practical applications.
It takes a network to get dinner: designing location-based systems to address local food needs BIBAFull-Text 519-528
  Lynn Dombrowski; Jed R. Brubaker; Sen H. Hirano; Melissa Mazmanian; Gillian R. Hayes
Based on an 18-month qualitative study that included the creation and testing of design considerations and a prototype location-based information system (LBIS), this research provides empirical insight into the daily practices of a wide variety of individuals working to address food insecurity in one U.S. county. Qualitative fieldwork reveals that nonprofit organizations in the food assistance ecology engage in location-based information practices that could be enhanced by the design of a LBIS. Two practices that would benefit from a collaborative LBIS are 1) practices of matching in which nonprofit workers help individuals who are seeking assistance to food resources and 2) practices of distribution in which nonprofit workers help organizations access and deliver food resources to clients. In order to support such practices across organizations the cooperative design component of this research suggests that an LIBS should: support the role of intermediaries who engage in practices of matching and distribution; provide interactive mapping tools that match resources to need; enable organizations to control visibility over specific data; and document work and impact. This research further suggests that designers should explore the wide variety of spatial patterns that must align and overlap such that ecologies of nonprofit organizations might synergistically work together to address pressing social needs.
An informed view on consent for UbiComp BIBAFull-Text 529-538
  Ewa Luger; Tom Rodden
Ubiquitous computing systems tend to be complex, seamless, data-driven and interactive. Reacting to both context, and users' implicit actions resulting from the lived experience, they cast all traces of human life as potential 'data'. To augment users' endeavours, such systems are necessarily embedded below the line of human attention, drawing upon new and highly sensitive types of data. This begs the question, where is the moment of user consent and how can this moment be truly informed? We would argue that it is time to revisit our design principles in respect of consent and redress the balance of agency towards the user. We draw upon a series of multidisciplinary interviews with experts to (a) reframe consent for ubicomp, and (b) offer three indicative principles, supportive of consent, for designers to 'balance' against system functionality. We hope that this will afford a new prism through which designers might make value judgements.

Location privacy

Locality and privacy in people-nearby applications BIBAFull-Text 539-548
  Eran Toch; Inbal Levi
People-Nearby applications are becoming a popular way for individuals to search for new social relations in their physical vicinity. This paper presents the results of a qualitative study, based on 25 interviews, examining how privacy and locality are managed in these applications. We describe how location is used as a grounding mechanism, providing a platform for honest and truthful signals in the challenging process of forming new social relations. We discuss our findings by suggesting theoretical frameworks that can be used to analyze the social space induced by the applications, as well as to inform the design of new technologies that foster the creation of new social ties.
Privacy manipulation and acclimation in a location sharing application BIBAFull-Text 549-558
  Shomir Wilson; Justin Cranshaw; Norman Sadeh; Alessandro Acquisti; Lorrie Faith Cranor; Jay Springfield; Sae Young Jeong; Arun Balasubramanian
Location sharing is a popular feature of online social networks, but challenges remain in the effective presentation of privacy choices to users, whose location sharing preferences are complex and diverse. One proposed approach for capturing these nuances builds on the observation that key attributes of users' location sharing preferences can be represented by a small number of privacy profiles, which can provide a basis for configuring individual preferences. However, the impact of this approach on how users view their privacy is relatively unknown. We present a study evaluating the impact of this approach on users' location sharing preferences and their satisfaction with the decisions made by their resulting settings. The results suggest that this approach can influence users to share significantly more without a substantial difference in comfort. This further suggests that the provision of profiles for privacy settings must be carefully considered, as they can substantially alter sharing behavior.
Protecting privacy for group nearest neighbor queries with crowdsourced data and computing BIBAFull-Text 559-562
  Tanzima Hashem; Mohammed Eunus Ali; Lars Kulik; Egemen Tanin; Anthony Quattrone
User privacy in location-based services (LBSs) has become an important research area. We introduce a new direction to protect user privacy that evaluates LBSs with crowdsourced data and computation and eliminates the role of a location-based service provider. We focus on the group nearest neighbor (GNN) query that allows a group to meet at their nearest point of interest such as a restaurant that minimizes the total or maximum distance of the group. We develop a crowdsource-based approach, called PrivateMeetUp, to evaluate GNN queries in a privacy preserving manner and implement a working prototype of PrivateMeetUp.

Mobile devices

Revisiting human-battery interaction with an interactive battery interface BIBAFull-Text 563-572
  Denzil Ferreira; Eija Ferreira; Jorge Goncalves; Vassilis Kostakos; Anind K. Dey
Mobile phone user interfaces typically show an icon to indicate remaining battery, but not the amount of time the device can be used for, often forcing users to make faulty estimates and predictions about battery life. Here we report on two studies that capture users' experiences with a user-centered battery interface design. In Study 1, we analyze 12 participants' use of mobile phones, demonstrating that mobile phone users do not know how or what to do to extend their mobile's battery life. We further identify the information they rely on to assess battery life. In Study 2, we use this information to design, prototype and evaluate an interactive battery interface (IBI) with another 22 participants. Our findings describe how users perceive battery life and how we used their mental models of mobile phone batteries to create IBI. Lastly, we report on the users' experiences and IBI's effect on battery lifetime, showing gains of approximately 27% over the course of a day.
FOCUS: a usable & effective approach to OLED display power management BIBAFull-Text 573-582
  Kiat Wee Tan; Tadashi Okoshi; Archan Misra; Rajesh Krishna Balan
In this paper, we present the design and implementation of Focus, a system for effectively and efficiently reducing power consumption of OLED displays on smartphones. These displays, while becoming exceedingly common still consume significant power. The key idea of Focus is that we use the notion of saliency to save display power by dimming portions of the applications that are less important to the user. We envision Focus being especially useful during low battery situations when usability is less important than power savings. We tested Focus using 15 applications running on a Samsung Galaxy S III and show that it saves, on average, between 23 to 34% of the OLED display power with little impact on task completion times. Finally, we present the results of a user study, involving 30 participants that shows that Focus, even with its dimming behaviour, is still quite usable.
DopLink: using the Doppler effect for multi-device interaction BIBAFull-Text 583-586
  Md Tanvir Islam Aumi; Sidhant Gupta; Mayank Goel; Eric Larson; Shwetak Patel
Mobile and embedded electronics are pervasive in today's environment. As such, it is necessary to have a natural and intuitive way for users to indicate the intent to connect to these devices from a distance. We present DopLink, an ultrasonic-based device selection approach. It utilizes the already embedded audio hardware in smart devices to determine if a particular device is being pointed at by another device (i.e., the user waves their mobile phone at a target in a pointing motion). We evaluate the accuracy of DopLink in a controlled user study, showing that, within 3 meters, it has an average accuracy of 95% for device selection and 97% for finding relative device position. Finally, we show three applications of DopLink: rapid device pairing, home automation, and multi-display synchronization.

Health II

Beyond self-monitoring: understanding non-functional aspects of home-based healthcare technology BIBAFull-Text 587-596
  Erik Grönvall; Nervo Verdezoto
Monitoring of health parameters in non-clinical settings is one strategy to address the increasingly aging population and age-related disabilities and diseases. However, challenges exist when introducing self-monitoring activities in people's everyday life. An active lifestyle can challenge the appropriation of healthcare technologies and people with comorbidity may have diverse but co-existing monitoring needs. In this paper, we seek to understand home-based health monitoring practices to better design and integrate them into people's everyday life. We perform an analysis of socio-technical complexities in home-based healthcare technologies through three case studies of self-monitoring: 1) pre-eclampsia (i.e. pregnancy poisoning), 2) heart conditions, and 3) preventive care. Through the analysis seven themes emerged (people, resources, places, routines, knowledge, control and motivation) that can facilitate the understanding of home-based healthcare activities. We present three modes of self-monitoring use and provide a set of design recommendations for future Ubicomp designs of home-based healthcare technology.
COPDTrainer: a smartphone-based motion rehabilitation training system with real-time acoustic feedback BIBAFull-Text 597-606
  Gabriele Spina; Guannan Huang; Anouk Vaes; Martijn Spruit; Oliver Amft
Patient motion training requires adaptive, personalized exercise models and systems that are easy to handle. In this paper, we evaluate a training system based on a smartphone that integrates in clinical routines and serves as a tool for therapist and patient. Only the smartphone's build-in inertial sensors were used to monitor exercise execution and providing acoustic feedback on exercise performance and exercise errors. We used a sinusoidal motion model to exploit the typical repetitive structure of motion exercises. A Teach-mode was used to personalize the system by training under the guidance of a therapist and deriving exercise model parameters. Subsequently, in a Train-mode, the system provides exercise feedback. We validate our approach in a validation with healthy volunteers and in an intervention study with COPD patients. System performance, trainee performance, and feedback efficacy were analysed. We further compare the therapist and training system performances and demonstrate that our approach is viable.
Making family care work: dependence, privacy and remote home monitoring telecare systems BIBAFull-Text 607-616
  John Vines; Stephen Lindsay; Gary W. Pritchard; Mabel Lie; David Greathead; Patrick Olivier; Katie Brittain
Supporting independent living for the ageing population in later life is an often-cited application area for ubiquitous computing. Telecare services such as remote monitoring systems are now coming onto the consumer market but there is little knowledge of the impact these technologies may have on relationships between family members and older relatives. We present findings from a live field trial of SHel -- a telecare system that allows nominated caregivers to remotely monitor activities -- in 17 older adult's homes. Interviews were conducted with the 17 older participants and 11 of their nominated caregivers before, during and after using the system. We establish that such technologies transform existing hidden care routines between family members into care work, and the impact they have upon the sense of independence and privacy of those who are being monitored in their home.

Computing in the home

Towards more natural digital content manipulation via user freehand gestural interaction in a living room BIBAFull-Text 617-626
  Sang-Su Lee; Jeonghun Chae; Hyunjeong Kim; Youn-kyung Lim; Kun-pyo Lee
Advances in dynamic gesture recognition technologies now make it possible to investigate freehand input techniques. This study tried to understand how users manipulate digital content on a distant screen by hand gesture interaction in a living room environment. While there have been many existing studies that investigate freehand input techniques, we developed and applied a novel study methodology based on a combination of both an existing user elicitation study and conventional Wizard-of-Oz study that involved another non-technical user for providing feedback. Through the study, many useful issues and implications for making freehand gesture interaction design more natural in a living room environment were generated which have not been covered in previous works. Furthermore, we could observe how the initial user-defined gestures are changed over time.
Home computing unplugged: why, where and when people use different connected devices at home BIBAFull-Text 627-636
  Fahim Kawsar; A. J. Bernheim Brush
We investigate how technology usage in homes has changed with the increasing prevalence of mobile devices including Tablets and Smart Phones. We logged Internet usage from 86 Belgium households to determine their six most common Internet Activities. Next, we surveyed households about what devices they own, how they share those devices, and which device they use for different Internet activities. We then conducted semi-structured interviews with 18 of 55 households that responded to the survey in which participants explained their device usage patterns and where they use technology in their home. Our findings suggest that the nature of online activity and social context influence device preference. Many participants reported that their Desktop PC is now a special purpose device, which they use only for specific activities such as working from home or online gaming. Compared to past studies, we observed technology use in many more locations in the home, most notably kitchens and bathrooms.
Weiser's dream in the Korean home: collaborative study of domestic roles, relationships, and ideal technologies BIBAFull-Text 637-646
  Hee Rin Lee; Selma Šabanovic
Following Bell and Dourish's call for a "ubicomp of the present," we visited 14 households in Korea, where Weiser's dreams come true, to study their social dynamics and domestic technologies as a part of these dynamics. We used a participatory research approach in which participants, acting as collaborative ethnographers and co-designers, chose how to describe their homes to us and which existing technologies to discuss. A qualitative analysis of the conversations identified two main themes. The first finding is the highly gendered nature of roles in the Korean home, influenced by traditional Confucian values and reinforced by contemporary neo-liberal norms. The second finding is that domestic technologies are used, adopted, and imagined in the context of these gendered social dynamics rather than just according to functional needs. In conclusion, we emphasize the need to attend to the social dynamics of the home in the design of politically sensitive domestic technologies, which will enable the inclusion of marginalized voices, such as women, in design.

Social computing I

Understanding user behavior at scale in a mobile video chat application BIBAFull-Text 647-656
  Lei Tian; Shaosong Li; Junho Ahn; David Chu; Richard Han; Qin Lv; Shivakant Mishra
Online video chat services such as Chatroulette and Omegle randomly match users in video chat sessions and have become increasingly popular, with tens of thousands of users online at anytime during a day. Our interest is in examining user behavior in the growing domain of mobile video, and in particular how users behave in such video chat services as they are extended onto mobile clients. To date, over four thousand people have downloaded and used our Android-based mobile client, which was developed to be compatible with an existing video chat service. The paper provides a first-ever detailed large scale study of mobile user behavior in a random video chat service over a three week period. This study identifies major characteristics such as mobile user session durations, time of use, demographic distribution and the large number of brief sessions that users click through to find good matches. Through content analysis of video and audio, as well as analysis of texting and clicking behavior, we discover key correlations among these characteristics, e.g., normal mobile users are highly correlated with using the front camera and with the presence of a face, whereas misbehaving mobile users have a high negative correlation with the presence of a face.
Adaptive information-sharing for privacy-aware mobile social networks BIBAFull-Text 657-666
  Igor Bilogrevic; Kévin Huguenin; Berker Agir; Murtuza Jadliwala; Jean-Pierre Hubaux
Personal and contextual information are increasingly shared via mobile social networks. Users' locations, activities and their co-presence can be shared easily with online "friends", as their smartphones already access such information from embedded sensors and storage. Yet, people usually exhibit selective sharing behavior depending on contextual attributes, thus showing that privacy, utility, and usability are paramount to the success of such online services. In this paper, we present SPISM, a novel information-sharing system that decides (semi-)automatically whether to share information with others, whenever they request it, and at what granularity. Based on active machine learning and context, SPISM adapts to each user's behavior and it predicts the level of detail for each sharing decision, without revealing any personal information to a third-party. Based on a personalized survey about information sharing involving 70 participants, our results provide insight into the most influential features behind a sharing decision. Moreover, we investigate the reasons for the users' decisions and their confidence in them. We show that SPISM outperforms other kinds of global and individual policies, by achieving up to 90% of correct decisions.
Adding an interactive display to a public basketball hoop can motivate players and foster community BIBAFull-Text 667-676
  Alan Chatham; Florian 'Floyd' Mueller
Interactive displays that aim to engage people through play have been successfully deployed in urban environments. However, there has been little work bringing interactive displays into existing public game spaces like outdoor basketball courts. To explore this, we designed an interactive display for a public half-court basketball hoop. We studied the impact of 3 different display modes over a 10-week period through interviews with players, spectators, and passers-by. Our findings suggest 3 dimensions for the design space of such interactive displays: balancing noticeability across different user groups, support for different play action, and support for connecting user groups. We also present 6 design tactics along these dimensions to help designers create engaging interactive displays for public game spaces. using it to facilitate engaging playful experiences.

Sustainability II

Storage-aware smartphone energy savings BIBAFull-Text 677-686
  David T. Nguyen; Gang Zhou; Xin Qi; Ge Peng; Jianing Zhao; Tommy Nguyen; Duy Le
In this paper, to our best knowledge, we are first to provide an experimental study on how storage techniques affect power levels in smartphones and introduce energy-efficient approaches to reduce energy consumption. We evaluate power degradation at several layers of block I/O, focusing on the block layer and device driver. At each level, we investigate the amount of energy that can be saved, and use that to design and implement a prototype with optimal energy savings named SmartStorage. The system tracks the run-time I/O pattern of a smartphone that is then matched with the closest pattern from the benchmark table. After having obtained the optimal parameters, it dynamically configures storage parameters to reduce energy consumption. We evaluate our prototype by using the 20 most popular Android applications, and our energy-efficient approaches achieve from 23% to 52% of energy savings compared to using the current techniques.
"We are not in the loop": resource wastage and conservation attitude of employees in Indian workplace BIBAFull-Text 687-696
  Mohit Jain; Ankit Agrawal; Sunil K. Ghai; Khai N. Truong; Deva P. Seetharam
Though rapid depletion of natural resources has become a global problem, most of the solutions developed to address it are based on studies done in the developed world. Moreover, the commercial sector is among the primary consumers of resources, yet research work has been mostly limited to residential users. We present a study exploring employees' perception, their beliefs and attitudes, towards environmental sustainability at workplaces in a developing region. To obtain broader context, we also conducted a focus group with the facility team members. Our study highlights that in spite of strong motivations to conserve, employees conservative actions are limited due to lack of controls, knowledge and responsibility. We identify new opportunities for design such as designing location specific buildings, removing inefficient choices, and building communal spaces, to facilitate conservation at workplaces.

Emotion and behavior II

MACH: my automated conversation coach BIBAFull-Text 697-706
  Mohammed (Ehsan) Hoque; Matthieu Courgeon; Jean-Claude Martin; Bilge Mutlu; Rosalind W. Picard
MACH -- My Automated Conversation coacH -- is a novel system that provides ubiquitous access to social skills training. The system includes a virtual agent that reads facial expressions, speech, and prosody and responds with verbal and nonverbal behaviors in real time. This paper presents an application of MACH in the context of training for job interviews. During the training, MACH asks interview questions, automatically mimics certain behavior issued by the user, and exhibit appropriate nonverbal behaviors. Following the interaction, MACH provides visual feedback on the user's performance. The development of this application draws on data from 28 interview sessions, involving employment-seeking students and career counselors. The effectiveness of MACH was assessed through a weeklong trial with 90 MIT undergraduates. Students who interacted with MACH were rated by human experts to have improved in overall interview performance, while the ratings of students in control groups did not improve. Post-experiment interviews indicate that participants found the interview experience informative about their behaviors and expressed interest in using MACH in the future.
Predicting audience responses to movie content from electro-dermal activity signals BIBAFull-Text 707-716
  Fernando Silveira; Brian Eriksson; Anmol Sheth; Adam Sheppard
The ability to assess fine-scale user responses has applications in advertising, content creation, recommendation, and psychology research. Unfortunately, current approaches, such as focus groups and audience surveys, are limited in size and scope. In this paper, we propose a combined biometric sensing and analysis methodology to leverage audience-scale electro-dermal activity (EDA) data for the purpose of evaluating user responses to video. We provide detailed characterization of how temporal physiological responses to video stimulus can be modeled, along with first-of-its-kind audience-scale EDA group experiments in uncontrolled real-world environments. Our study provides insights into the techniques used to analyze EDA, the effectiveness of the different temporal features, and group dynamics of audiences. Our experiments demonstrate the ability to classify movie ratings with accuracy of over 70% on specific films. Results of this study suggest the ability to assess emotional reactions of groups using minimally invasive sensing modalities in uncontrolled environments.

Social computing II

Making a home for social media BIBAFull-Text 717-720
  Clint Heyer; Irina Shklovski; Nanna Jensen
In this paper we report on the design and implementation of an initial prototype to explore how to better situate in the home social media content individually generated by family members. We considered whether existing infrastructure and practices of social media might be leveraged to offer new kinds of shared family experiences. We found that families perceived the system to be "cosy" and intimate, especially in contrast to Facebook, and as a result 'shared to care'. While aspects of the design had a strong role to play in faciliating this perception, participants enacted their own boundaries of sharing and disclosure based on pre-existing practices and attitudes toward social technologies. The study demonstrated that there are productive design opportunities in home systems that can leverage content via a broad range of social media applications.
An unsupervised learning approach to social circles detection in ego bluetooth proximity network BIBAFull-Text 721-724
  Jiangchuan Zheng; Lionel M. Ni
Understanding a user's social interactions in the physical world proves important in building context-aware ubiquitous applications. A good way towards that objective is to categorize people to whom a user is socially related into what we call as social circles. In this note, we propose a novel unsupervised approach that learns from the Bluetooth (BT) sensed data recording one's dynamic proximity relations with others to identify her social circles, each of which is formed along a semantically coherent aspect. For each circle we learn its members as well as the temporal dimensions along which it is formed. Our method is innovative in that it well overcomes data sparsity by information sharing, and allows for circle overlaps which is common in reality. Experiments on real data demonstrate the effectiveness of our method, and also show the potentials of relational mobile data in sensing personal behaviors beyond personal data.
Musical embrace: exploring social awkwardness in digital games BIBAFull-Text 725-728
  Amy Huggard; Anushka De Mel; Jayden Garner; Cagdas 'Chad' Toprak; Alan Chatham; Florian 'Floyd' Mueller
Socially awkward experiences are often looked upon as something to be avoided. However, examples from the non-digital entertainment domain suggest that social awkwardness can also facilitate engaging experiences. Yet there has been little research into exploring social awkwardness in digital games. In response, we present Musical Embrace, a digital game that promotes close physical proximity through the use of a novel pillow-like controller to facilitate socially awkward play between strangers. Through our observations from demonstrating Musical Embrace at a number of events, we have derived a set of strategies to engage players by "facilitating social awkwardness", allowing players to "transform social awkwardness" while also letting players "take control of social awkwardness". With our work we hope to inspire game designers to consider the potential of social awkwardness in digital games and guide them when using it to facilitate engaging play experiences.

Food and nutrition

Combining embedded accelerometers with computer vision for recognizing food preparation activities BIBAFull-Text 729-738
  Sebastian Stein; Stephen J. McKenna
This paper introduces a publicly available dataset of complex activities that involve manipulative gestures. The dataset captures people preparing mixed salads and contains more than 4.5 hours of accelerometer and RGB-D video data, detailed annotations, and an evaluation protocol for comparison of activity recognition algorithms. Providing baseline results for one possible activity recognition task, this paper further investigates modality fusion methods at different stages of the recognition pipeline: (i) prior to feature extraction through accelerometer localization, (ii) at feature level via feature concatenation, and (iii) at classification level by combining classifier outputs. Empirical evaluation shows that fusing information captured by these sensor types can considerably improve recognition performance.
Technological approaches for addressing privacy concerns when recognizing eating behaviors with wearable cameras BIBAFull-Text 739-748
  Edison Thomaz; Aman Parnami; Jonathan Bidwell; Irfan Essa; Gregory D. Abowd
First-person point-of-view (FPPOV) images taken by wearable cameras can be used to better understand people's eating habits. Human computation is a way to provide effective analysis of FPPOV images in cases where algorithmic approaches currently fail. However, privacy is a serious concern. We provide a framework, the privacy-saliency matrix, for understanding the balance between the eating information in an image and its potential privacy concerns. Using data gathered by 5 participants wearing a lanyard-mounted smartphone, we show how the framework can be used to quantitatively assess the effectiveness of four automated techniques (face detection, image cropping, location filtering and motion filtering) at reducing the privacy-infringing content of images while still maintaining evidence of eating behaviors throughout the day.
FoodBoard: surface contact imaging for food recognition BIBAFull-Text 749-752
  Cuong Pham; Daniel Jackson; Johannes Schoening; Tom Bartindale; Thomas Ploetz; Patrick Olivier
We describe FoodBoard, an instrumented chopping board that uses optical fibers and embedded camera imaging to identify unpackaged ingredients during food preparation on its surface. By embedding the sensing directly, and robustly, in the surface of a chopping board we also demonstrate how surface contact optical sensing can be used to realize the portability and privacy required of technology used in a setting such as a domestic kitchen. FoodBoard was subjected to a close to real-world evaluation in which 12 users prepared actual meals. FoodBoard compared favourably with existing unpackaged food recognition systems, classifying a larger number of distinct food ingredients (12 incl. meat, fruit, vegetables) with an average accuracy of 82.8%.

Public displays

Crowdsourcing on the spot: altruistic use of public displays, feasibility, performance, and behaviours BIBAFull-Text 753-762
  Jorge Goncalves; Denzil Ferreira; Simo Hosio; Yong Liu; Jakob Rogstadius; Hannu Kukka; Vassilis Kostakos
This study is the first attempt to investigate altruistic use of interactive public displays in natural usage settings as a crowdsourcing mechanism. We test a non-paid crowdsourcing service on public displays with eight different motivation settings and analyse users' behavioural patterns and crowdsourcing performance (e.g., accuracy, time spent, tasks completed). The results show that altruistic use, such as for crowdsourcing, is feasible on public displays, and through the controlled use of motivational design and validation check mechanisms, performance can be improved. The results shed insights on three research challenges in the field: i) how does crowdsourcing performance on public displays compare to that of online crowdsourcing, ii) how to improve the quality of feedback collected from public displays which tends to be noisy, and iii) identify users' behavioural patterns towards crowdsourcing on public displays in natural usage settings.
The media façade toolkit: prototyping and simulating interaction with media façades BIBAFull-Text 763-772
  Sven Gehring; Elias Hartz; Markus Löchtefeld; Antonio Krüger
Digital technologies are rapidly finding their way into urban spaces. One prominent example is media façades. Due to their size, visibility and their technical capabilities, they offer great potential for interaction and for becoming the future displays of public spaces. To explore their potential, researchers have recently started to develop interactive applications for various media façades. Existing development tools are mostly tailored to one specific media façade in one specific setting. They usually provide limited means to incorporate interaction by a user, and the applications developed are limited to running on only one particular media façade. In this paper, we present a flexible, generalized media façade toolkit, which is capable of mimicking arbitrary media façade installations. The toolkit is capable of running interactive applications on media façades with different form factors, sizes and technical capabilities. Furthermore, it ensures application portability between different media façades and offers the possibility of providing interactivity by enabling user input with different modalities and different interaction devices.
Ambient recommendations in the pop-up shop BIBAFull-Text 773-776
  Gonzalo Garcia-Perate; Nicholas Dalton; Ruth Conroy-Dalton; Duncan Wilson
In this paper we present the design and first-stage analysis of a purposely built, smart, pop-up wine shop. Our shop learns from visitors' choices and recommends wine using collaborative filtering and ambient feedback displays integrated into its furniture. Our ambient recommender system was tested in a controlled laboratory environment. We report on the qualitative feedback and between subjects study, testing the influence the system had in wine choice behavior. Participants reported the system helpful, and results from our empirical analysis suggest it influenced buying behavior.

Positioning II

Detecting and correcting WiFi positioning errors BIBAFull-Text 777-786
  Yuki Tsuda; Quan Kong; Takuya Maekawa
Recent advances in GPS and WiFi-based positioning technologies for mobile phones have triggered many location-based services. However, GPS positioning quickly drains a phone's battery and cannot be used indoors. On the other hand, WiFi positioning provides energy-efficient indoor and outdoor positioning with reasonable accuracy. However, WiFi positioning sometimes makes large errors caused by various reasons, e.g., the movement of reference WiFi access points. In this paper we attempt to detect and correct such errors automatically by performing outlier detection in time series.
   So, we solve this problem by comparing a user's current measurement at time T with her coordinate point at time T predicted from her past coordinate history, and judging whether the current measurement is correct or not by computing the distance between the measurement location and the predicted location. However, it is difficult to predict the user's coordinates accurately with a single prediction method (predictor) because the user's context (e.g., migration speed and sparseness of past coordinates) greatly affects predictor performance. We thus design a context-aware error detection method by employing an ensemble of predictors that have different strengths and weaknesses.
Opportunistic position update protocols for mobile devices BIBAFull-Text 787-796
  Patrick Baier; Frank Dürr; Kurt Rothermel
Many location-based applications such as geo-social networks rely on location services storing mobile object positions. To update positions on location servers, position update protocols are used. On the one hand, these protocols decide when an update has to be sent to ensure a certain quality of position information. On the other hand, they try to minimize the energy consumption of the mobile device by reducing communication to a minimum.
   In this paper, we show how to improve the energy efficiency of different update protocols by taking the energy characteristics of the mobile network interface into account. In particular, we show that the energy consumption can be reduced on average by 70% using an opportunistic update strategy sending position updates together with messages of other applications. We present a Markov model to predict the arrival of messages and an online optimization algorithm calculating an optimized schedule to send position updates.
An RF doormat for tracking people's room locations BIBAFull-Text 797-800
  Juhi Ranjan; Yu Yao; Kamin Whitehouse
Many occupant-oriented smarthome applications such as automated lighting, heating and cooling, and activity recognition need room location information of residents within a building. Surveillance based tracking systems used to track people in commercial buildings, are privacy invasive in homes. In this paper, we present the RF Doormat -- a RF threshold system that can accurately track people's room locations by monitoring their movement through the doorways in the home. We also present a set of guidelines and a visualization to easily and rapidly setup the RF-Doormat system on any doorway. To evaluate our system, we perform 580 doorway crossings across 11 different doorways in a home. Results indicate that our system can detect doorway crossings made by people with an average accuracy of 98%. To our knowledge, the RF Doormat is the first highly accurate room location tracking system that can be used for long time periods without the need for privacy invasive cameras.


UniPad: orchestrating collaborative activities through shared tablets and an integrated wall display BIBAFull-Text 801-810
  Stefan Kreitmayer; Yvonne Rogers; Robin Laney; Stephen Peake
UniPad is a face-to-face, digital simulation for use in classroom settings that runs on shared tablets and a wall display. The goal is to encourage students to talk, collaborate and make decisions together in real-time, by switching between working on shared 'small group' devices and a 'whole classroom' public display -- instead of working by themselves using their own device. It is intended to improve peer discussion and teacher involvement by focusing and constraining shared attention at different stages of an activity. The domain for this study is finance management. The system was designed using an iterative, participatory design method with expert finance educators and then trialed using an in-the-wild study at a school. The findings show how the set-up helped in facilitating verbal participation in the classroom. We discuss how lightweight, multi-device shared technology systems, such as UniPad, can be designed and used for a range of classroom activities.
Embracing calibration in body sensing: using self-tweaking to enhance ownership and performance BIBAFull-Text 811-820
  Rose Johnson; Nadia Bianchi-Berthouze; Yvonne Rogers; Janet van der Linden
Calibration is a necessary step in many sensor-based ubicomp applications to prepare a system for operation. Particularly when dealing with sensors for movement-based interaction calibration is required to individualize the system to the person's body. However, calibration is often viewed as a tedious necessity of a purely technical nature. In this paper we argue that calibration can be used as a valuable and informative step for users molding a technology for their own use. We explain this through two case studies that use body sensing technologies to teach physical skills. Our studies show that calibration can be used by teachers and pupils to set goals. We argue that demystifying calibration and designing to expose the intentions of the technology and its functioning can be beneficial for users, allowing them to shape technology to be in tune with their bodies rather than changing their body to fit the technology.