Attentional lapses while individuals are engaged in activities can have critical effects on their performance. EEG sensors offer the potential to monitor brain activity and detect such lapses in-situ. However, advances in automatic detection are limited, notably due to the scarcity of validated EEG data for training. In this work, we explore the design space of lapses-of-attention detectors using EEG signals, framing it as a binary classification problem. We introduce an EEG dataset with two validated attention levels acquired through a controlled experiment (N = 24) involving reading tasks with and without auditory distractions. We evaluated fifteen detectors using three different EEG feature extraction techniques, and five classifier models. Models using filterbank-CSP features yielded the highest median per-participant detection accuracy of 96%. Limited-resource analyses further indicate the Beta frequency band is the most informative for attention detection, and highlight detection can be achieved with only four EEG channels. Taken together, our findings inform on the feasibility and design of automatic attention detection for brain-computer interfaces utilizing simpler EEG devices.

Micro Data Videos (mDVs) are up to one-minute data-driven vertical video clips for mobile devices. Widely adopted on social media platforms, mDVs hold significant potential for disseminating information. Despite their growing prevalence, little is known about their components and how they are designed. Thus, two studies were conducted. Study 1 analyzed 40 mDVs and revealed their narrative components. Study 2 examined, through design sessions with design experts, how such components are assembled to craft storyboards for mDVs. The diverse narrative styles of mDVs render them flexible and suitable for multiple topics and purposes. Further, many include a “Linker" directing viewers to external online resources. Participants approached their design in a structural yet iterative manner with emphasis on setting up a “hook” in the opening seconds to capture attention. We summarize and share common design practices used in creating mDVs, an increasingly important medium in data storytelling.

Optical-see-through (OST) augmented reality headsets offer users the flexibility to access relevant data visualizations anytime and anywhere. However, the appearance of content displayed on OST displays varies in colour and transparency depending on the environment they are viewed in, potentially leading to interpretation challenges. We present the findings of a psychophysical study (N = 24), aimed at assessing the impact of two environmental factors – lighting intensity and background colour – on user performance and colour perception accuracy in a visualization and colour-matching task using an OST headset. Our results suggest the effect of background colour on visualization interpretation is notable only under bright lighting conditions. Interestingly, participants perceived low-colour-contrast scenarios as more challenging, although their performance did not decline. Additionally, visualization colours were perceptibly and distinctly mismatched, but did not blend with the background colours. Finally, we discuss visual comfort and colour coding in the context of designing ubiquitous visualizations on OST displays, highlighting open challenges.

Data Videos (DV) have been used in a diverse set of fields. However, the possibility of utilizing them with social robots for further improving the viewer’s engagement is yet to be examined. While social robots have been used in various presentation-related applications, there is also a lack of design instructions on how to better utilize them. Hence with this early work, we explore the possibility of using social robots as potential DV presenters through; a quantitative analysis of the factors of visible presenters in DVs, and a testing phase of these factors via four group design sessions involving experienced designers. From the DV analysis, we identified 12 unique techniques across four main factors. The observations from the group design sessions show that these findings overlap with the design practices of experienced designers when designing robotic presentations.

Research suggest negative feedback could guide users’ behaviours effectively in Human-AI interactions. However, providing negative feedback, relative to positive counterparts, can be more challenging in any type of communication. This paper delves into the potential of a social robot in delivering negative feedback for improving the in-class learning experience for children. With child participants (12 and younger), we conducted three co-design studies to investigate their preferred facial expressions of a social robot, Haru, which can identify them being distracted (i.e., undesirable behaviour), and redirect their attention back to their task with the facial expressions. Altogether, results indicated that children do not want to see conventional punishing expressions (e.g., angry faces) as a reaction to their undesirable behaviour. Instead, they preferred pleasant ones (e.g., funny, cute). Further, the importance of using realistic stimuli for studies and the co-design approach, as well as the challenges of interpreting children’s drawing responses, are discussed.

Tabletop social robots are becoming increasingly common, not only as social companions but as presenters and orators of information. We present an exploration of utilizing robots as a multimodal presentation tool to communicate data-driven facts. Our exploration is inspired by the wealth of research on data videos (DVs) as these have become mainstream sources for swiftly conveying data-driven information to a mass audience. We first analyze 48 DVs that contain visible narrators (presenters who are visible in the video frames) as our source for understanding the techniques used to convey factual information via presenters. Twelve dimensions across four factors (presenter-grounded; narrative-grounded; viewer-engagement-related; and data-visualization-related) were identified. These factors were carefully arranged in designing presenters to engage the audience with the video content. We adapt these findings to the design of an expressive social tabletop robot that can communicate data-driven knowledge to its audience. Supported by four design sessions with expert content creators and designers, we provide nine design implications for designing multimodal presentations with an expressive tabletop social robot. We conclude with the possible application potentials of this unique data presentation modality.

Conditionally automated vehicles provide unique opportunities for drivers to engage in non-driving-related tasks (NDRTs); however, drivers must remain prepared to respond to take-over requests. This paper explores design challenges and potential solutions for supporting reading as an NDRT in SAE Level 3 vehicles. Specifically, we assess two prominent text-presentation techniques: vertical scrolling text presentation (VSTP) and rapid serial visual presentation (RSVP), exploring both in conjunction with their integration with auditory speech displays (ASD). A driving simulation study involving N = 32 participants revealed that RSVP surpassed VSTP in regaining situational awareness, as indicated by lower average braking actuation, and was also preferred by participants. The integration of ASDs with both techniques reduced perceived cognitive workload and improved the user experience, albeit with compromised lateral control. Our findings can help advance the design of human-centered interfaces for reading in conditionally automated vehicles.

The current pandemic showed us the importance of swiftly disseminating data-based information to the masses of people. This study explores an affect-centered narrative to convey data-driven messages regarding contact tracing apps (CTAs) using video as a medium (i.e., data video). A between-subjects online study compared the effect of three storytelling approaches on viewers' perception. A video developed by Google was selected as the baseline video (Control Condition; 2min 23s) due to its high quality and relevance to CTAs. The central messages of this baseline video were; a) how CTAs work, and b) how safe and effective CTAs are. Infographics supporting these messages were then added to the baseline video (the second condition; 3min 19s); this was a simple data video (DV), and it did not intend to induce specific emotional experiences in participants (i.e., cognition-centered video). Finally, an affect-focused DV (AFDV) was also created by emphasizing the emotion-based narrative aspect of the message (the third condition; 4min 6s). In this video, three cute human-like cartoon characters were introduced. Viewers in this condition needed to process both cognitive and affective information. Note all three videos (i.e., control video, DV, and AFDV) conveyed identical messages. Participants watched one of these three videos only once, and we explored the video effect on their perception. Our results repeatedly indicated the potential benefits of including affect in data storytelling.

Data Videos (DVs), or animated infographics that tell stories with data, are becoming increasingly popular. Despite their potential to induce attitude change, little is explored about how to produce effective DVs. This paper describes two studies that explored factors linked to the potential of health DVs to improve viewers’ behavioural change intentions. We investigated: 1) how viewers’ affect is linked to their behavioural change intentions; 2) how these affect are linked to the viewers’ personality traits; 3) which attributes of DVs are linked to their persuasive potential. Results from both studies indicated that viewers’ negative affect lowered their behavioural change intentions. Individuals with higher neuroticism exhibited higher negative affect and were harder to convince. Finally, Study 2 proved that providing any solutions to the health problem, presented in the DV, made the viewers perceive the videos as more actionable while lowering their negative affect, and importantly, induced higher behavioural change intentions.

Multiple embedded sensors enable smartwatch apps to amass large amounts of interrelated time-series data simultaneously, such as heart rate, oxygen levels or steps walked. Visualizing multiple interlinked datasets is possible on smartphones but remains challenging on small smartwatch displays. We propose a new technique, the Space-Filling Line Graph (SF-LG), that preserves the key visual properties of time-series graphs while making available space on the display to augment such graphs with additional information. Results from our first study (N=30) suggest that, while SF-LG makes available additional space on the small display, it also enables effective (i.e. quick and accurate) comprehension of key line graph tasks. We next implement a greedy algorithm to embed auxiliary information in the most suitable regions on the display. In a second study (N=27), we find that participants are efficient at locating and linking interrelated content using SF-LG in comparison to two baselines approaches. We conclude with guidelines for smartwatch space maximization for visual displays.

The use of crowdsourcing platforms for data collection in HCI research is attractive in their ability to provide rapid access to large and diverse participant samples. As a result, several researchers have conducted studies investigating the similarities and differences between data collected through crowdsourcing and more traditional, laboratory-style data collection. We add to this body of research by examining the feasibility of conducting social acceptability studies via crowdsourcing. Social acceptability can be a key determinant for the early adoption of emerging technologies, and as such, we focus our investigation on social acceptability for Head-Worn Display (HWD) input modalities. Our results indicate that data collected via a crowdsourced experiment and a laboratory-style setting did not differ at a statistically significant level. These results provide initial support for crowdsourcing platforms as viable options for conducting social acceptability research.

The popularity of head-worn displays (HWD) technologies such as Virtual Reality (VR) and Augmented Reality (AR) headsets is growing rapidly. To predict their commercial success, it is essential to understand the acceptability of these new technologies, along with new methods to interact with them. In this vein, the evaluation of social acceptability of interactions with these technologies has received significant attention, particularly from the performer's (i.e., user's) viewpoint. However, little work has considered social acceptability concerns from observers' (i.e., spectators') perspective. Although HWDs are designed to be personal devices, interacting with their interfaces are often quite noticeable, making them an ideal platform to contrast performer and observer perspectives on social acceptability. Through two studies, this paper contrasts performers' and observers' perspectives of social acceptability interactions with HWDs under different social contexts. Results indicate similarities as well as differences, in acceptability, and advocate for the importance of including both perspectives when exploring social acceptability of emerging technologies. We provide guidelines for understanding social acceptability specifically from the observers' perspective, thus complementing our current practices used for understanding the acceptability of interacting with these devices.