Cities serve as vital hubs of economic activity and knowledge generation and dissemination. As such, cities bear a significant responsibility to uphold environmental protection measures while promoting the welfare and living comfort of their residents. There are diverse views on the development of smart cities, from integrating Information and Communication Technologies into urban environments for better operational decisions to supporting sustainability, wealth, and comfort of people. However, for all these cases, data are the key ingredient and enabler for the vision and realization of smart cities. This article explores the challenges associated with smart city data. We start with gaining an understanding of the concept of a smart city, how to measure that the city is a smart one, and what architectures and platforms exist to develop one. Afterwards, we research the challenges associated with the data of the cities, including availability, heterogeneity, management, analysis, privacy, and security. Finally, we discuss ethical issues. This article aims to serve as a “one-stop shop” covering data-related issues of smart cities with references for diving deeper into particular topics of interest.

Living with Parkinson’s Disease introduces a range of significant challenges into one’s daily life. While medical interventions exist to overcome some of these challenges, patient self-care techniques often form an essential complement to the treatments recommended by medical doctors. Knowledge on these self-care techniques often originates from those living with Parkinson’s themselves or their close caregivers, as they have the knowledge and experience required to assess self-care techniques. This so-called ‘patient knowledge’ is usually exchanged in peer meetings or discussion forums. Although vital to the Parkinson’s Disease community, this information is often difficult to access due to its unstructured format and the difficulty of navigating through online forums. We present an online tool that allows for contributing, assessing, and finally discovering Parkinson’s Disease self-care techniques. The custom discovery tool was populated with self-care knowledge by over 300 people with Parkinson’s and dozens of their carers, spanning areas such as daily well-being and using assistive equipment. Then, we invited patients to explore the discover features in a smaller scale trial. While well-received, our deployment highlighted several challenges that we further discuss in this paper. Overall, our study contributes to crowdsourced digital health solutions and provides both design and research implications to this challenging domain with a vulnerable user group.

Abstract Smartphones have become an integral part of people’s everyday lives. Smartphones are used across all household locations, including in the bed at night. Smartphone screens and other displays emit blue light, and exposure to blue light can affect one’s sleep quality. Thus, smartphone use prior to bedtime could disrupt the quality of one’s sleep, but research lacks quantitative studies on how smartphone use can influence sleep. This study combines smartphone application use data from 75 participants with sleep data collected by a wearable ring. On average, the participants used their smartphones in bed for 322.8 s (5 min and 22.8 s), with an IQR of 43.7–456. Participants spent an average of 42% of their time in bed using their smartphones (IQR of 5.87–55.5%). Our findings indicate that smartphone use in bed has significant adverse effects on sleep latency, awake time, average heart rate, and HR variability. We also find that smartphone use does not decrease sleep quality when used outside of bed. Our results indicate that intense smartphone use alone does not negatively affect well-being. Since all smartphone users do not use their phones in the same way, extending the investigation to different smartphone use types might yield more information than general smartphone use. In conclusion, this paper presents the first investigation of the association between smartphone application use logs and detailed sleep metrics. Our work also validates previous research results and highlights emerging future work.

A new generation of wearable devices now enable end-users to keep track of their sleep patterns. This paper reports on a longitudinal study of 82 participants who used a state-of-the-art sleep tracking ring for an average of 65 days. We conducted interviews and questionnaires to understand changes to their lifestyle, their perceptions of the tracked information and sleep, and the overall experience of using an unobtrusive sleep tracking device. Our results indicate that such a device is suitable for long-term sleep tracking and helpful in identifying detrimental lifestyle elements that hinder sleep quality. However, tracking one's sleep can also introduce stress or physical discomfort, potentially leading to adverse outcomes. We discuss these findings in light of related work and highlight the near-term research directions that the rapid commoditisation of sleep tracking technology enables.

Depression is a prevalent mental disorder. Current clinical and self-reported assessment methods of depression are laborious and incur recall bias. Their sporadic nature often misses severity fluctuations. Previous research highlights the potential of in-situ quantification of human behaviour using mobile sensors to augment traditional methods of depression management. In this paper, we study whether self-reported mood scores and passive smartphone and wearable sensor data could be used to classify people as depressed or non-depressed. In a longitudinal study, our participants provided daily mood (valence and arousal) scores and collected data using their smartphones and Oura Rings. We computed daily aggregations of mood, sleep, physical activity, phone usage, and GPS mobility from raw data to study the differences between the depressed and non-depressed groups and created population-level Machine Learning classification models of depression. We found statistically significant differences in GPS mobility, phone usage, sleep, physical activity and mood between depressed and non-depressed groups. An XGBoost model with daily aggregations of mood and sensor data as predictors classified participants with an accuracy of 81.43% and an Area Under the Curve of 82.31%. A Support Vector Machine using only sensor-based predictors had an accuracy of 77.06% and an Area Under the Curve of 74.25%. Our results suggest that digital biomarkers are promising in differentiating people with and without depression symptoms. This study contributes to the body of evidence supporting the role of unobtrusive mobile sensor data in understanding depression and its potential to augment depression diagnosis and monitoring.

Public controversies around the unethical use of personal data are increasing, spotlighting data ethics as an increasingly important field of study. MyData is a related emerging vision that emphasizes individuals’ control of their personal data. In this paper, we investigate people’s perceptions of various data management scenarios by measuring the perceived ethicality and level of felt concern concerning the scenarios. We deployed a set of 96 unique scenarios to an online crowdsourcing platform for assessment and invited a representative sample of the participants to a second-stage questionnaire about the MyData vision and its potential in the field of healthcare. Our results provide a timely investigation into how topical data-related practices affect the perceived ethicality and the felt concern. The questionnaire analysis reveals great potential in the MyData vision. Through the combined quantitative and qualitative results, we contribute to the field of data ethics.

Abstract Despite large investments in smartwatch development, the market growth remains smaller than forecasted. The purpose of smartwatch use remains unclear, indicated by the lack of large-scale adoption. Thus, we aim to better understand the early adoption and everyday smartwatch use. We investigate a diverse usage data of smartwatches logged over a period of up to 14 months from 79 individuals between December 2015 and March 2017, one of the largest wearable datasets collected. First, we identify both explorative and accepted behaviours that users exhibit and further investigate how the individual usage traits and features differ between the two categories. Our analysis offers an insightful perspective on how smartwatch use evolves organically. Our results improve our shared understanding of smartwatch use and users adapting their use of smartwatch over time to match the capabilities of the technology by validating numerous findings from previous literature.

Spiral drawing has been utilized for years as a clinical tool to observe tremors and other abnormal movements in the assessment of different movement disorders. Specifically, in Parkinson's Disease (PD), patients' motor functionalities are measured by various tests, and spiral drawing is one of the proven techniques for assessing the severity of PD motor symptoms. Traditionally, this test is performed on pen and paper, and visually assessed by a clinician. There have been successful efforts for digitizing this test on tablets. Here, we describe a smartphone-based digitized version of the spiral drawing test. Moreover, we introduce a square-shaped drawing to solve an identified challenge of a smaller screen estate: finger occlusion while drawing. Both approaches are evaluated with 8 Parkinson's Disease patients and 6 age-matching control participants. Based on earlier studies and our data, we select suitable motion parameters for quantifying the task. Our results show an observable, statistically difference in performance between users with Parkinson's Disease and the control group in drawing accuracy.

As smartphones are increasingly an integral part of daily life, recent literature suggests a deeper relationship between personality traits and smartphone usage. However, this relationship depends on many complex factors such as geographic location, demographics, or cultural influence, just to name a few. These factors provide crucial knowledge for e.g. usage support, recommendations, marketing, general usage improvements. We use six months of application usage data from 739 Android smartphone user together with the IPIP 50-item Big Five personality traits questionnaire. As our main contribution, we show that even category-level aggregated application usage can predict Big Five traits at up to 86%–96% prediction fit in our sample. Our results show the effect of personality traits on application usage (mean error improvement on random guess 17.0%). We also identify which application usage data best describe the Big Five personality traits. Our work enables future personality-driven research, and shows that when studying personality, application categories can provide sufficient predictions in general traits.

Mobile self-reports are a popular technique to collect participant labelled data in the wild. While literature has focused on increasing participant compliance to self-report questionnaires, relatively little work has assessed response accuracy. In this paper, we investigate how participant context can affect response accuracy and help identify strategies to improve the accuracy of mobile self-report data. In a 3-week study we collect over 2,500 questionnaires containing both verifiable and non-verifiable questions. We find that response accuracy is higher for questionnaires that arrive when the phone is not in ongoing or very recent use. Furthermore, our results show that long completion times are an indicator of a lower accuracy. Using contextual mechanisms readily available on smartphones, we are able to explain up to 13% of the variance in participant accuracy. We offer actionable recommendations to assist researchers in their future deployments of mobile self-report studies.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. Measuring the symptoms and progress of the disease, and medication effectiveness is currently performed using subjective measures and visual estimation. We developed and evaluated a mobile application, STOP for tracking hand's motor symptoms, and a medication journal for recording medication intake. We followed 13 PD patients from two countries for a 1-month long real-world deployment. We found that PD patients are willing to use digital tools, such as STOP, to track their medication intake and symptoms, and are also willing to share such data with their caregivers and medical personnel to improve their own care.

Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-time and location-dependent urban sensing data. Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost. While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants' behavioral patterns or sensing data correlation. In this paper, we perform an extensive literature review of learning-assisted optimization approaches in MCS. Specifically, from the perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation. Furthermore, we discuss how different techniques can be combined to form a complete solution. In the end, we point out existing limitations, which can inform and guide future research directions.

Parkinson's disease (PD) is the second most common neurodegenerative disorder, impacting an estimated seven to ten million people worldwide. It is commonly accepted that improving medication adherence alleviates symptoms and maintains motor capabilities. Not following the medication regimen (e.g., skipping or over-medicating) may worsen side-effects, which mislead clinicians and patients. We developed and evaluated a mobile application, STOP, for screening the PD symptoms and medication intake. It contains a game for tracking the PD symptoms, and a medication journal for recording medical intake and adherence. We conducted a 1-month long real-world deployment with 13 PD patients from two countries. We found that the application medication adherence tracking provides non-bias information, and users are receptive to share such data with their care and medical personnel.

We seek to quantify smartwatch use, and establish differences and similarities to smartphone use. Our analysis considers use traces from 307 users that include over 2.8 million notifications and 800,000 screen usage events, and we compare our findings to previous work that quantifies smartphone use. The results show that smartwatches are used more briefly and more frequently throughout the day, with half the sessions lasting less than 5 seconds. Interaction with notifications is similar across both types of devices, both in terms of response times and preferred application types. We also analyse the differences between our smartwatch dataset and a dataset aggregated from four previously conducted smartphone studies. The similarities and differences between smartwatch and smartphone use suggest effect on usage that go beyond differences in form factor.

While the population is aging the role of information and communication technology (ICT) has grown in elderly care. This development has brought versatile ICT-related supportive systems to professionals and laymen working with aging people. The current study analyzed how professionals in elderly care perceived their workflow challenges before new ICT is developed and implemented to support their work. The results of this study are set to inform the design of a novel ICT system for a sheltered care home.

Previous work suggests that Quantified-Self applications can retain long-term usage with motivational methods. These methods often require intermittent attention requests with manual data input. This may cause unnecessary burden to the user, leading to annoyance, frustration and possible application abandonment. We designed a novel method that uses on-screen alert dialogs to transform recurrent smartphone usage sessions into moments of data contributions and evaluate how accurately machine learning can reduce unintended interruptions. We collected sensor data from 48 participants during a 4-week long deployment and analysed how personal device usage can be considered in scheduling data inputs. We show that up to 81.7% of user interactions with the alert dialogs can be accurately predicted using user clusters, and up to 75.5% of unintended interruptions can be prevented and rescheduled. Our approach can be leveraged by applications that require self-reports on a frequent basis and may provide a better longitudinal QS experience.

Digital marketing is increasingly moving from desktop (e.g., browser) to mobile environments (e.g., within mobile applications). The means for delivering ads however, remains largely unchanged: banners and videos. In this work, we explore transforming ad delivery methods to the mobile environment while mitigating issues causing frustration and distractions to the users, evident in both web and mobile marketing. We demonstrate that softly enforcing interaction with the ad - with minimal usable screen space reduction - can improve user's attitude towards mobile advertising. Brand recollection is also influenced via increased interactions with the ad delivery method.

It is becoming increasingly easy for researchers to develop context-aware applications for smartphones. A perennial challenge, however, is to convince a large number of people to install them and donate contextual data for scientific purposes. Our empirical study seeks to address this challenge by investigating how people's perception and attitude affect their willingness to donate context data to researchers and quantifies the effects of social signals on donation action-taking. Our findings indicate that the perceived need for donation and perceived organization reputation are key determinants in deciding whether to donate: people with altruistic personality do not necessarily donate if they cannot see the need to take an action. Furthermore, we provide evidence that even if people indicate a willingness to donate, they are hesitant to take action towards donating data unless catalysts like social signals (hints about the actions of others) are present.

Mobile offloading is a promising technique to aid the constrained resources of a mobile device. By offloading a computational task, a device can save energy and increase the performance of the mobile applications. Unfortunately, in existing offloading systems, the opportunistic moments to offload a task are often sporadic and short-lived. We overcome this problem by proposing a social-aware hybrid offloading system (HyMobi), which increases the spectrum of offloading opportunities. As a mobile device is always co-located to at least one source of network infrastructure throughout of the day, by merging cloudlet, device-to-device and remote cloud offloading, we increase the availability of offloading support. Integrating these systems is not trivial. In order to keep such coupling, a strong social catalyst is required to foster user's participation and collaboration. Thus, we equip our system with an incentive mechanism based on credit and reputation, which exploits users’ social aspects to create offload communities. We evaluate our system under controlled and in-the-wild scenarios. With credit, it is possible for a device to create opportunistic moments based on user's present need. As a result, we extended the widely used opportunistic model with a long-term perspective that significantly improves the offloading process and encourages unsupervised offloading adoption in the wild.

Researchers who analyse smartphone usage logs often make the assumption that users who lock and unlock their phone for brief periods of time (e.g., less than a minute) are continuing the same "session" of interaction. However, this assumption is not empirically validated, and in fact different studies apply different arbitrary thresholds in their analysis. To validate this assumption, we conducted a field study where we collected user-labelled activity data through ESM and sensor logging. Our results indicate that for the majority of instances where users return to their smartphone, i.e., unlock their device, they in fact begin a new session as opposed to continuing a previous one. Our findings suggest that the commonly used approach of ignoring brief standby periods is not reliable, but optimisation is possible. We therefore propose various metrics related to usage sessions and evaluate various machine learning approaches to classify gaps in usage.

Research claims that users value the battery life of their smartphones, but no study to date has attempted to quantify battery value and how this value changes according to users' current context and needs. Previous work has quantified the monetary value that smartphone users place on their data (e.g., location), but not on battery life. Here we present a field study and methodology for systematically measuring the monetary value of smartphone battery life, using a reverse second-price sealed-bid auction protocol. Our results show that the prices for the first and last 10% battery segments differ substantially. Our findings also quantify the tradeoffs that users consider in relation to battery, and provide a monetary model that can be used to measure the value of apps and enable fair ad-hoc sharing of smartphone resources.

We develop a Markov state transition model of smartphone screen use. We collected use traces from real-world users during a 3-month naturalistic deployment via an app-store. These traces were used to develop an analytical model which can be used to probabilistically model or predict, at runtime, how a user interacts with their mobile phone, and for how long. Unlike classification-driven machine learning approaches, our analytical model can be interrogated under unlimited conditions, making it suitable for a wide range of applications including more realistic automated testing and improving operating system management of resources.

An important facet of smartphone's usage is internet. Everything works flawlessly, as long as you have a good internet connection. A smartphone's functionality is immediately limited by the absence of internet: applications are not up-to-date; instant chat messages are not delivered when intended, or one is unable to get directions. Besides internet performance tuning, research has been scarce in leveraging users' internet access routines to improve smartphone's usage. By understanding smartphone internet availability, one may utilise this information to minimise data costs and improve users' experience while using internet-enabled applications. Our paper provides insight into when is it likely that an individual user is online, based on personal connectivity routines.

There is a limited amount of scavenging alternatives for smartphones. We assess the feasibility of using indoor light to extend smartphones' battery life. We build a prototype charger that demonstrates that indoor light scavenging is a practical method that can substantially extend battery life on smartphones. The results show that it is feasible and practical to extend battery life with this energy harvesting method. We finally discuss certain obstacles that need to be overcome, especially the redesign of operating systems to account for energy harvesting.

We present a revisitation analysis of smartphone use to investigate the question: do smartphones induce usage habits? We analysed three months of application launch logs from 165 users in naturalistic settings. Our analysis reveals distinct clusters of applications and users which share similar revisitation patterns. However, we show that much of smartphone usage on a macro-level is very similar to web browsing on desktops, and thus argue that smartphone usage is driven by innate service needs rather than technology characteristics. On the other hand, on a micro-level we identify unique characteristics in smartphone usage, and we present a rudimentary model that accounts for 92% in the variability of our smartphone use.

Driven by curiosity and our own three diverse smartphone application usage datasets, we sought to unpack the nuances of mobile device use by revisiting two recent Mobile HCI studies [1, 17]. Our goal was to add to our broader understanding of smartphone usage by investigating if differences in mobile device usage occurred not only across our three datasets, but also in relation to prior work. We found differences in the top-10 apps in each dataset, in the durations and types of interactions as well as in micro-usage patterns. However, it proved very challenging to attribute such differences to a specific factor or set of factors: was it the time frame in which the studies were executed? The recruitment procedure? The experimental method? Using our somewhat troubled analysis, we discuss the challenges and issues of conducting mobile research of this nature and reflect on caveats related to the replicability and generalizability of such work.

Smartphone users do not fully know what their apps do. For example, an applications' network usage and underlying security configuration is invisible to users. In this paper we introduce Securacy, a mobile app that explores users' privacy and security concerns with Android apps. Securacy takes a reactive, personalized approach, highlighting app permission settings that the user has previously stated are concerning, and provides feedback on the use of secure and insecure network communication for each app. We began our design of Securacy by conducting a literature review and in-depth interviews with 30 participants to understand their concerns. We used this knowledge to build Securacy and evaluated its use by another set of 218 anonymous participants who installed the application from the Google Play store. Our results show that access to address book information is by far the biggest privacy concern. Over half (56.4%) of the connections made by apps are insecure, and the destination of the majority of network traffic is North America, regardless of the location of the user. Our app provides unprecedented insight into Android applications' communications behavior globally, indicating that the majority of apps currently use insecure network connections.

In this paper we present Game of Words, a crowdsourcing game for public displays that allows the creation of a keyword dictionary to describe locations. It relies on crowdsourcing and gamification to identify, filter, and rank keywords based on their relevance to the location of the public display itself. We demonstrate that crowdsourcing on public displays can leverage users' knowledge of their environment, can work with a generic gaming task, and can be deployed on displays with multiple concurrent services. Our analysis shows that our approach has important benefits, such as the ability to identify undesired input, provide words of high semantic relevance, as well as a broader scope of keywords. Finally, our analysis also demonstrates that the chosen game design coped well with the challenges of this complex setting (i.e. public urban space) by disincentivising incorrect use of the system.

This study employs hierarchical cluster analysis, strategic diagrams and network analysis to map and visualize the intellectual landscape of the CHI conference on Human Computer Interaction through the use of co-word analysis. The study quantifies and describes the thematic evolution of the field based on a total of 3152 CHI articles and their associated 16035 keywords published between 1994 and 2013. The analysis is conducted for two time periods (1994-2003, 2004-2013) and a comparison between them highlights the underlying trends in our community. More significantly, this study identifies the evolution of major themes in the discipline, and highlights individual topics as popular, core, or backbone research topics within HCI.

Projective tests are personality tests that reveal individuals' emotions (e.g., Rorschach inkblot test). Unlike direct question-based tests, projective tests rely on ambiguous stimuli to evoke responses from individuals. In this paper we develop one such test, designed to be delivered automatically, anonymously and to a large community through public displays. Our work makes a number of contributions. First, we develop and validate in controlled conditions a quantitative projective test that can reveal emotions. Second, we demonstrate that this test can be deployed on a large scale longitudinally: we present a four-week deployment in our university's public spaces where 1431 tests were completed anonymously by passers-by. Third, our results reveal strong diurnal rhythms of emotion consistent with results we obtained independently using the Day Reconstruction Method (DRM), literature on affect, well-being, and our understanding of our university's daily routine.

We present Pulse, a wireless magnetic communication protocol for smartphones. Pulse is designed for off-the-shelf Android smartphones with magnetometers, and encodes data in magnetic fields. We present the design and evaluation of Pulse in various conditions (e.g., different voltages, number of transfer channels). The system provides security due to its short range (~1 cm), it can reach a speed of up to 44 bits per second, and it is possible to run it on most mobile phones with a magnetometer. We present our evaluation and discuss practical use cases where Pulse can be used today.

The rapid growth of the Ubicomp field has recently raised concerns regarding its identity. These concerns have been compounded by the fact that there exists a lack of empirical evidence on how the field has evolved until today. In this study we applied co-word analysis to examine the status of Ubicomp research. We constructed the intellectual map of the field as reflected by 6858 keywords extracted from 1636 papers published in the HUC, UbiComp and Pervasive conferences during 1999--2013. Based on the results of a correspondence analysis we identify two major periods in the whole corpus: 1999--2007 and 2008--2013. We then examine the evolution of the field by applying graph theory and social network analysis methods to each period. We found that Ubicomp is increasingly focusing on mobile devices, and has in fact become more cohesive in the past 15 years. Our findings refute the assertion that Ubicomp research is now suffering an identity crisis.

Research suggests smartphone users face 'application overload', but literature lacks an in-depth investigation of how users manage their time on smartphones. In a 3-week study we collected smartphone application usage patterns from 21 participants to study how they manage their time interacting with the device. We identified events we term application micro-usage: brief bursts of interaction with applications. While this practice has been reported before, it has not been investigated in terms of the context in which it occurs (e.g., location, time, trigger and social context). In a 2-week follow-up study with 15 participants, we captured participants? context while micro-using, with a mobile experience sampling method (ESM) and weekly interviews. Our results show that about approximately 40% of application launches last less than 15 seconds and happen most frequently when the user is at home and alone. We further discuss the context, taxonomy and implications of application micro-usage in our field. We conclude with a brief reflection on the relevance of short-term interaction observations for other domains beyond mobile phones.

In an increasingly connected world, users access personal or shared data, stored "in the cloud" (e.g., Dropbox, Skydrive, iCloud) with multiple devices. Despite the popularity of cloud storage services, little work has focused on investigating cloud storage users' Quality of Experience (QoE), in particular on mobile devices. Moreover, it is not clear how users' context might affect QoE. We conducted an online survey with 349 cloud service users to gain insight into their usage and affordances. In a 2-week follow-up study, we monitored mobile cloud service usage on tablets and smartphones, in real-time using a mobile-based Experience Sampling Method (ESM) questionnaire. We collected 156 responses on in-situ context of use for Dropbox on mobile devices. We provide insights for future QoE-aware cloud services by highlighting the most important mobile contextual factors (e.g., connectivity, location, social, device), and how they affect users' experiences while using such services on their mobile devices.

Research is increasingly highlighting the potential for situated crowdsourcing to overcome some crucial limitations of online crowdsourcing. However, it remains unclear whether a situated crowdsourcing market can be sustained, and whether worker supply responds to price-setting in such a market. Our work is the first to systematically investigate workers' behaviour and response to economic incentives in a situated crowdsourcing market. We show that the market-based model is a sustainable approach to recruiting workers and obtaining situated crowdsourcing contributions. We also show that the price mechanism is a very effective tool for adjusting the supply of labour in a situated crowdsourcing market. Our work advances the body of work investigating situated crowdsourcing.

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.

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.

In this paper we present HotCity, a service that demonstrates how collecting and mining the interactions that users make with the urban environment through social networks, can help tourists better plan activities, through sharing the collectively generated social context of a smart, connected city, as a background layer to mapped POI. The data for our service stems from the collection and analysis of 1-month worth of collected human-physical environment interactions (i.e., Foursquare check-ins) data for Oulu, a medium-sized city in Finland, where our service is deployed in ubiquitous public displays. Our analysis demonstrates that a good model of the city's dynamics can be built despite the low popularity of Foursquare amongst locals. Our findings from the field-based trial of the HotCity service yield several useful insights and important contributions. We found that the method of using a heatmap as an intermediate layer of environmental context does not negatively affect the experience of users at the cognitive level, compared with a more traditional map and POI type of interface, where temporal aspects of context are not present. In the concluding sections, we discuss how this cloud-based service can also be used in a variety of ubiquitous computing platforms.

Thanks to the rapid development of mobile technologies, smartphones allow people to be reachable anywhere and anytime. In addition to the benefits for end users, researchers and developers can also benefit from the powerful devices that participants potentially carry on a daily basis. This minitrack workshop brings together researchers with an interest on using mobile devices as instruments to collect data and conduct mobile user studies, with a focus on understanding human-behavior, routines and gathering context.

Open mobile platforms such as Android currently suffer from the existence of multiple versions, each with its own peculiarities. This makes the comprehensive testing of interactive applications challenging. In this paper we present Testdroid, an online platform for conducting scripted user interface tests on a variety of physical Android handsets. Testdroid allows developers and researchers to record test scripts, which along with their application are automatically executed on a variety of handsets in parallel. The platform reports the outcome of these tests, enabling developers and researchers to quickly identify platforms where their systems may crash or fail. At the same time the platform allows us to identify more broadly the various problems associated with each handset, as well as frequent programming mistakes.

Much research in ubiquitous computing assumes that a user's phone will be always on and at-hand, for collecting user context and for communicating with a user. Previous work with the previous generation of mobile phones has shown that such an assumption is false. Here, we investigate whether this assumption about users' proximity to their mobile phones holds for a new generation of mobile phones, smart phones. We conduct a data collection field study of 28 smart phone owners over a period of 4 weeks. We show that in fact this assumption is still false, with the within arm's reach proximity being true close to 50% of the time, similar to the earlier work. However, we also show that smart phone proximity within the same room (arm+room) as the user is true almost 90% of the time. We discuss the reasons for these phone proximities and the implications of this on the development of mobile phone applications, particularly those that collect user and environmental context, and delivering notification to users. We also show that we can accurately predict the proximity at the arm level and arm+room level with 75 and 83% accuracy, respectively, with features simple to collect and model on a mobile phone. Further we show that for several individuals who are almost always within the arm+room level, we can predict this level with over 90% accuracy.