Data physicalizations have the potential for more natural, intuitive and embodied interactions with data that can engage people with data in a playful way compared to traditional screen and mouse-based interactions. Such interactions are useful to make data understandable and interesting to interact with, especially for special user groups such as children. In this research, we explored how direct manipulation of physical tangibles as an interaction method affects children’s engagement with data, the understanding of data and the overall child-data experience. We compared two new tangible manipulation-based interaction methods: TM-E (Tangible Manipulation Emptying) and TM-F (Tangible Manipulation Filling) designed to create more natural interactions with air quality data and compared them with a slider-based interaction. A user study (N=36) with young adolescents revealed that the newly introduced interaction methods were effective in conveying and recalling air quality information and were positively received by the participants. We also observed that the interaction metaphor impacts the overall user experience. These lessons learned are relevant to the design of physicalizations fostering public awareness, for example, in the context of sustainable development.

The global COVID-19 pandemic left few facets of our lives unchanged, including the possibility to play organised sports. Many gyms and sports clubs had to shut down due to social distancing measures, which resulted in reduced physical activity. Co-located group sports that promote social interaction and fun became almost impossible. This lack of physical interaction combined with social isolation and confinement have shown negative impacts on physical and mental well-being of people (for example, lack of motivation, anxiety, low immunity, low academic performance, negative mood and feelings) especially in children and young adults. Recent research therefore recommends exploring more creative approaches that allow physical activities during a pandemic, especially the ones that increase social interaction and engagement and allow users to share the same physical space. Towards this, we developed SixFeet, a novel digital-physical sports platform that allows its participants to play rigorous, collaborative sports – at a six feet distance. SixFeet ensures that the players are 1.5m away from each other at all times without them noticing it or without them having to worry about the social distancing measures. We developed a prototypical implementation of SixFeet and conducted a pilot study to evaluate its technical feasibility and practicality as well as obtain a first insight into user experience with the platform. Results show that participants never came within 1.5 meters of one another, felt connected nevertheless and were physically exerted. This paper presents the design and architecture of SixFeet, results of the preliminary user study, and a discussion on the versatility of SixFeet in accommodating training for different sports in times of a pandemic.

While lab-based approaches to evaluation of location-based services (LBSs) allow faster, cost-effective and less complicated evaluation of many aspects, they are limited in evaluating certain aspects, especially the ones related to usability and user experience (UX). Variations in the location information quality can negatively affect location-based UX. Lab-based evaluations are limited in factoring users' actual context, which includes location quality variations into the evaluation process. Therefore, to evaluate actual location-based UX, we need to evaluate LBSs in-the-wild with users under variations in the quality of location information. Currently, there is a lack of standard tools, methods, and frameworks for this purpose. Motivated by this factor, in this paper, we describe the Location Uncertainty Injection Framework (LUIF) for evaluating mobile LBSs in-the-wild with users under location quality variations. We also present the results of a study conducted to gain initial feedback on the potential use cases of the framework.

Degraded GPS signals can negatively affect users of mobile Pedestrian Navigation Applications. Visualization of location uncertainty has emerged as a solution to this problem that has proven beneficial to users. However, there are only a small number of different visualizations developed for this purpose. In addition, their actual impact on facilitating navigation in GPS degraded situations has not been studied well. We designed two new visualizations of location uncertainty and compared them to existing ones in terms of efficiency and user acceptance. A field-based user study(N=18) showed that the two new visualizations significantly reduced the number of wrong turns. Users preferred the landmark-based visualization most and ranked it as the most helpful visualization for judging their true location in the environment when faced with GPS degradations. Despite participants being unfamiliar with the new visualizations, the task completion time, subjective task load and user experience for them were not significantly different from the more familiar state-of-the-art visualization.