Abdallah El Ali

EyeSee360: designing a visualization technique for out-of-view objects in head-mounted augmented reality

Head-mounted displays allow user to augment reality or dive into a virtual one. However, these 3D spaces often come with problems due to objects that may be out of view. Visualizing these out-of-view objects is useful under certain scenarios, such as situation monitoring during ship docking. To address this, we designed a lo-fi prototype of our EyeSee360 system, and based on user feedback, subsequently implemented EyeSee360. We evaluate our technique against well-known 2D off-screen object visualization techniques (Arrow, Halo, Wedge) adapted for head-mounted Augmented Reality, and found that EyeSee360 results in lowest error for direction estimation of out-of-view objects. Based on our findings, we outline the limitations of our approach and discuss the usefulness of our developed lo-fi prototyping tool.

Identification and Classification of Usage Patterns in Long-Term Activity Tracking

Activity trackers are frequently used in health and well-being, but their application in effective interventions is challenging. While research for reasons of use and non-use is ongoing, little is known about the way activity trackers are used in everyday life and over longer periods. We analyzed data of 104 individuals over 14,413 use days, and in total over 2.5 years. We describe general tracker use, periodic changes and overall changes over time, and identify characteristic patterns. While the use of trackers shows large individual heterogeneity, from our findings we could identify and classify general patterns for activity tracker use such as try-and-drop, slow-starter, experimenter, hop-on hop-off, intermittent and power user. Our findings contribute to the body of knowledge towards the successful design of effective health technologies, health interventions, and long-term health applications.

Visualizing out-of-view objects in head-mounted augmented reality

Various off-screen visualization techniques that point to off-screen objects have been developed for small screen devices. A similar problem arises with head-mounted Augmented Reality (AR) with respect to the human field-of-view, where objects may be out of view. Being able to detect so-called out-of-view objects is useful for certain scenarios (e.g., situation monitoring during ship docking). To augment existing AR with this capability, we adapted and tested well-known 2D off-screen object visualization techniques (Arrow, Halo, Wedge) for head-mounted AR. We found that Halo resulted in the lowest error for direction estimation while Wedge was subjectively perceived as best. We discuss future directions of how to best visualize out-of-view objects in head-mounted AR.

NaviLight: investigating ambient light displays for turn-by-turn navigation in cars

Car navigation systems typically combine multiple output modalities; for example, GPS-based navigation aids show a real-time map, or feature spoken prompts indicating upcoming maneuvers. However, the drawback of graphical navigation displays is that drivers have to explicitly glance at them, which can distract from a situation on the road. To decrease driver distraction while driving with a navigation system, we explore the use of ambient light as a navigation aid in the car, in order to shift navigation aids to the periphery of human attention. We investigated this by conducting studies in a driving simulator, where we found that drivers spent significantly less time glancing at the ambient light navigation aid than on a GUI navigation display. Moreover, ambient light-based navigation was perceived to be easy to use and understand, and preferred over traditional GUI navigation displays. We discuss the implications of these outcomes on automotive personal navigation devices.

Peripheral Light Cues for In-Vehicle Task Resumption

The increasing amount of in-vehicle information systems has led to handling multiple tasks simultaneously. This increases mental workload and consequently causes interruptions and distraction from driving. In this paper, we propose applying peripheral light cues to support in-vehicle task resumption. We ran a driving simulator study in which we tested the effect of peripheral light cues on task resumption. Results showed that, during the presence of cues, users required less time to resume the interrupted task and made fewer errors at resumption. Moreover, participants found the cues to be helpful for retrieval of their deferred intentions, and eased resumption.

All about Acceptability?: Identifying Factors for the Adoption of Data Glasses

Innovations often trigger objections before becoming widely accepted. This paper assesses whether a familiarisation over time can be expected for data glasses, too. While user attitudes towards those devices have been reported to be prevalently negative [14], it is still unclear, to what extent this initial, negative user attitude might impede adoption. However, indepth understanding is crucial for reducing barriers early in order to gain access to potential benefits from the technology. With this paper we contribute to a better understanding of factors affecting data glasses adoption, as well as current trends and opinions. Our multiple-year case study (N=118) shows, against expectations, no significant change towards a more positive attitude between 2014 and 2016. We complement these findings with an expert survey (N=51) investigating prognoses, challenges and discussing the relevance of social acceptability. We elicit and contrast a controversial spectrum of expert opinions, and assess whether initial objections can be overwritten. Our analysis shows that while social acceptability is considered relevant for the time being, utility and usability are more valued for long-term adoption.

Measuring, Understanding, and Classifying News Media Sympathy on Twitter after Crisis Events

This paper investigates bias in coverage between Western and Arab media on Twitter after the November 2015 Beirut and Paris terror attacks. Using two Twitter datasets covering each attack, we investigate how Western and Arab media differed in coverage bias, sympathy bias, and resulting information propagation. We crowdsourced sympathy and sentiment labels for 2,390 tweets across four languages (English, Arabic, French, German), built a regression model to characterize sympathy, and thereafter trained a deep convolutional neural network to predict sympathy. Key findings show: (a) both events were disproportionately covered (b) Western media exhibited less sympathy, where each media coverage was more sympathetic towards the country affected in their respective region (c) Sympathy predictions supported ground truth analysis that Western media was less sympathetic than Arab media (d) Sympathetic tweets do not spread any further. We discuss our results in light of global news flow, Twitter affordances, and public perception impact.

Face2Emoji: Using Facial Emotional Expressions to Filter Emojis

One way to indicate nonverbal cues is by sending emoji (e.g., 😂), which requires users to make a selection from large lists. Given the growing number of emojis, this can incur user frustration, and instead we propose Face2Emoji, where we use a users facial emotional expression to filter out the relevant set of emoji by emotion category. To validate our method, we crowdsourced 15,155 emoji to emotion labels across 308 website visitors, and found that our 202 tested emojis can indeed be classified into seven basic (including Neutral) emotion categories. To recognize facial emotional expressions, we use deep convolutional neural networks, where early experiments show an overall accuracy of 65% on the FER-2013 dataset. We discuss our future research on Face2Emoji, addressing how to improve our model performance, what type of usability test to run with users, and what measures best capture the usefulness and playfulness of our system.

Badum Tss!: A DIY Paper-based Interaction to Augment Everyday Situations with Sound Effects

The DIY rollable soundboard is a paper-based instant button device, driven by a Raspberry Pi that allows for easily creating simple sound effects. With sound effects like a Badum Tss (the sound of drums which often appears for a bad joke in comedy shows), it augments everyday situations with a suitable sound while users are on the go. Since paper is quiet cheap and almost readily available, paper does not cause any inhibitions to touch it and create sounds. Unlike a simple instant button application on a private smartphone, the rollable soundboard invites people to explore it and to entertain their social environment. In this paper, we describe the design process for the development of a rollable, portable soundboard from the conceptual design down to the functional prototype and first usability tests, and discuss relevant areas of future work.

VapeTracker: Tracking Vapor Consumption to Help E-cigarette Users Quit

Despite current controversy over e-cigarettes as a smoking cessation aid, we present early work based on a web survey (N=249) that shows that some e-cigarette users (46.2%) want to quit altogether, and that behavioral feedback that can be tracked can fulfill that purpose. Based on our survey findings, we designed VapeTracker, an early prototype that can attach to any e-cigarette device to track vaping activity. We discuss our future research on vaping cessation, addressing how to improve our VapeTracker prototype, ambient feedback mechanisms, and the future inclusion of behavior change models to support quitting e-cigarettes.