As Human-Robot Interaction (HRI) and Human-Robot Collaboration (HRC) integrate into society, there's a growing need for flexible, safe, and cost-effective methods to evaluate HRI and HRC from a human perspective. Current evaluation techniques, which often rely on physical hardware, are limited by high costs, safety risks, and low replicability. To address these challenges, we introduce Platform for Evaluating Human-Robot Collaboration and Interaction in Virtual Environments (PEHRCIVE), a software tool built with Unity that leverages Virtual Reality (VR) to provide a safe, flexible, and low-cost research platform. PEHRCIVE provides a customizable, immersive VR environment for HRI and HRC research, based on a collaborative interlocking block assembly task, three distinct collaborative robots (Kuka LBR iiwa, Rethink Robotics Sawyer, and Baxter), and sequential and simultaneous collaboration modes. Features include an easily modifiable external configuration file, a robust data logging system with timestamps and task-specific metrics, and customizable answered-in-VR questionnaires for data collection. PEHRCIVE was validated with 36 participants by collecting physiological and psychological data and responses to the Godspeed Questionnaire Series and VR Presence questionnaire. Participants reported a high sense of presence and a positive VR experience, confirming the platform's effectiveness as a research tool. PEHRCIVE facilitates the design, testing, and evaluation of HRI and HRC experiments more efficiently and safely, accelerating progress for the research community.

Autonomously tailoring content to a set of pre-determined affective patterns has long been considered the holy grail of affect-aware human-computer interaction at large. The experience-driven procedural content generation framework realises this vision by searching for content that elicits a certain experience pattern to a user. In this paper, we propose a novel reinforcement learning (RL) framework for generating affect-tailored content, and we test it in the domain of racing games. Specifically, the experience-driven RL (EDRL) framework is given a target arousal trace, and it then generates a racetrack that elicits the desired affective responses for a particular type of player. EDRL leverages a reward function that assesses the affective pattern of any generated racetrack from a corpus of arousal traces. Our findings suggest that EDRL can accurately generate affect-driven racing game levels according to a designer's style and outperforms search-based methods for personalised content generation. The method is not only directly applicable to game content generation tasks but also employable broadly to any domain that uses content for affective adaptation.

Providing care to individuals with chronic diseases benefits from a multidisciplinary approach and longitudinal symptom, event, and disease monitoring, in and out of clinical facilities. Technological advancements, including the ubiquitous presence of sensors and devices, present opportunities to collect large amounts of data and extract evidence-based insights about the patient and disease. Nevertheless, practical examples of clinical utility of those technologies remain sparse, and in specific focus areas (e.g, insights from a single device). This paper explores the challenges and opportunities of multidisciplinary clinical dashboards to support clinicians caring for people with chronic diseases. We report on a focus group and co-design workshops with a multidisciplinary team of clinicians and HCI researchers. We offer insights into how technological outcomes and visualizations can enhance clinical practice and the intricacies of information-sharing dynamics. We discuss the potential of dashboards to trigger actions in clinical settings and emphasize the benefits of customizable dashboards.

Typing on mobile devices is a common and complex task. The act of typing itself thereby encodes rich information, such as the typing method, the context it is performed in, and individual traits of the person typing. Researchers are increasingly using a selection or combination of experience sampling and passive sensing methods in real-world settings to examine typing behaviours. However, there is limited understanding of the effects these methods have on measures of input speed, typing behaviours, compliance, perceived trust and privacy. In this paper, we investigate the tradeoffs of everyday data collection methods. We contribute empirical results from a four-week field study (N=26). Here, participants contributed by transcribing, composing, passively having sentences analyzed and reflecting on their contributions. We present a tradeoff analysis of these data collection methods, discuss their impact on text-entry applications, and contribute a flexible research platform for in the wild text-entry studies.