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.

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.

In this paper, a deep learning approach is proposed to accurately position wheeled vehicles in Global Navigation Satellite Systems (GNSS) deprived environments. In the absence of GNSS signals, information on the speed of the wheels of a vehicle (or other robots alike), recorded from the wheel encoder, can be used to provide continuous positioning information for the vehicle, through the integration of the vehicle's linear velocity to displacement. However, the displacement estimation from the wheel speed measurements are characterised by uncertainties, which could be manifested as wheel slips or/and changes to the tyre size or pressure, from wet and muddy road drives or tyres wearing out. As such, we exploit recent advances in deep learning to propose the Wheel Odometry neural Network (WhONet) to learn the uncertainties in the wheel speed measurements needed for correction and accurate positioning. The performance of the proposed WhONet is first evaluated on several challenging driving scenarios, such as on roundabouts, sharp cornering, hard-brake and wet roads (drifts). WhONet's performance is then further and extensively evaluated on longer-term GNSS outage scenarios of 30s, 60s, 120s and 180s duration, respectively over a total distance of 493 km. The experimental results obtained show that the proposed method is able to accurately position the vehicle with up to 93% reduction in the positioning error of its original counterpart after any 180s of travel. WhONet's implementation can be found at https://github.com/onyekpeu/WhONet.