Denzil Ferreira

AWARE: Mobile Context Instrumentation Framework

We present a mobile instrumentation toolkit, AWARE, an open-source effort to develop an extensible and reusable platform for capturing, inferring, and generating context on mobile devices. Mobile phones are sensor-rich but resource-constrained, and therefore several considerations need to be addressed when creating a research tool that ensures problem-free context collection. We demonstrate how AWARE can mitigate researchers’ effort when building mobile data-logging tools and context-aware applications, with minimal battery impact. By encapsulating implementation details of sensor data retrieval and exposing the sensed context as higher-level abstractions, AWARE shifts the focus from software development to data analysis, both quantitative and qualitative. We have evaluated AWARE in several case studies and discuss its use, power consumption, and scalability.

CHI 1994-2013: mapping two decades of intellectual progress through co-word analysis

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.

Testdroid: automated remote UI testing on Android

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.

Lessons Learned from Large-Scale User Studies: Using Android Market as a Source of Data

User studies with mobile devices have typically been cumbersome, since researchers have had to recruit participants, hand out or configure devices, and offer incentives and rewards. The increasing popularity of application stores has allowed researchers to use such mechanisms to recruit participants and conduct large-scale studies in authentic settings with relatively little effort. Most researchers who use application stores do not consider the side-effects or biases that such an approach may introduce. The authors summarize prior work that has reported experiences from using application stores as a recruiting, distribution and study mechanism, and also present a case study of a 4-week long study using the Android Market to deploy an application to over 4000 users that collected data on their mobile phone charging habits. The authors synthesize their own experiences with prior reported findings to discuss the challenges, advantages, limitations and considerations of using application stores as a recruitment and distribution approach for conducting large-scale studies.

Understanding the Challenges of Mobile Phone Usage Data

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.

Revisitation analysis of smartphone app use

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.

Are Smartphones Ubiquitous? An In-depth Survey of Smartphone Adoption by Seniors

Substantial ongoing research now uses smartphones as a research platform for various studies and interventions. With the aging population becoming a frequent focus of research, an increasing number of studies and projects attempt to develop technological interventions for the elderly population. The extent to which the elderly population (i.e., seniors) adopts and uses smartphones is not clear. Many studies acknowledge that todays seniors are not particularly keen on using smartphones, but in the near future we can expect this trend to change.

Identity crisis of ubicomp?: mapping 15 years of the field's development and paradigm change

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.

Towards multi-application public interactive displays

Public displays are becoming a common sight in the urban landscape and they are increasingly being equipped with interaction components such as touch screens. In addition, the Web and its set of enabling technologies are attractive for realizing applications for public displays. We argue that to develop multi-application public displays, then services generally need to be easy to develop, robust, and be easy to deploy and maintain. In this position paper we present a virtual machine-based Web application platform, for decentralized deployment of interactive services on heterogeneous public displays, which satisfies the aforementioned requirements. We also report on some usage experiences of utilizing the platform in a network of large public displays, which we have deployed in a mid-size city.

Modelling smartphone usage: a markov state transition model

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.