Högskolan i Skövde

Background: Against the backdrop of escalating global humanitarian crises, innovative didactic simulations are becoming increasingly important. A promising alternative to traditional classroom-based didactics for learning psychological first aid (PFA) prior to humanitarian crises is to use generative artificial intelligence (GenAI)-based virtual patient (VP) systems. However, there is limited research on the use of GenAI-based VP systems for crisis support teams' learning. The aim of this study was to describe crisis support teams' technological openness and learning attitudes toward the GenAI-based VP system called Crisis Support Virtual Reality (VR).

Method: This study employed an action research design, originating from the collaboration with the innovation project Crisis Support VR in the Västra Götaland region of western Sweden. A total of 32 participants completed the Technological Openness and Learning Attitudes (TOLA) survey. Quantitative data were analyzed using descriptive statistics, while qualitative responses were employed as illustrative quotes.

Results: The participants felt generally positive about practicing PFA for serious events/humanitarian crises using the Crisis support VR and believe that it is a good complement to practicing PFA. Perceived learning benefits for participants through observing the avatar´s facial and body expressions in applied GenAI-based VP system received lowest score among all items.

Discussion: Crisis Support VR represents an innovative didactic simulation tool for learning PFA, serving as a valuable complement to traditional classroom-based didactics. Future research should evaluate its effects on learning outcomes and, in doing so, contribute to preparedness and response capacity during humanitarian crises.

Predicting human upper extremity reaching motion in 3D space can support adaptive interactions with computer-controlled systems (robots and virtual avatars), and applications in ergonomics and rehabilitation. This study compares two predictive approaches: an optimization-based method (OPM) and a proposed heuristic method (SFM) that integrates steering dynamics path planning, an adaptive velocity model, and inverse kinematics. Both methods were validated against motion capture data from ten participants performing four reach tasks. Predictions and inter-subject variability were evaluated for path, velocity, and upper extremity joint configuration using root mean square error and dynamic time warping. Results show that SFM more accurately predicts spatial path and velocity, whereas OPM achieves greater precision in joint angle estimation. As input, OPM requires the initial and end posturesand the task duration, while SFM needs the initial posture, initial and target end-effector positions, and initial and estimated peak velocity. These results highlight trade-offs between accuracy and behavioral variability when selecting motion prediction methods.

The use of social robots in many sectors of society is predicted to progressively increase. Therefore, exploring how expectations play a role in and change users’ experiences when interacting with these robots over time is necessary. From an interpretative and insight-driven approach, our aim was to explore how humans experience in-person interactions with the social robot Pepper, which was equipped with the OpenAI GPT-3 language model. Qualitative data from 62 video recordings of the interactions with Pepper and post-test interviews were collected from 31 participants. An experiential reflexive thematic analysis was applied. The main findings include various levels of interaction quality, different interaction strategies, and elements influencing the users’ expectations and experiences, which were synthesized into a coherent framework. It appears that the participants adapted their interaction strategies based on their expectations and the perceived capability of the robot, which influenced their experiences. This reveals that positive user experience is not solely determined by interaction quality, showing the interplay among these aspects when interacting with a social robot. To conclude, our findings underscore the intricate nature of the role of user expectations and experiences in social human–robot interaction. The work adds complementary qualitative approaches to the Human–Robot Interaction community to provide additional insights on interacting with social robots.

Accurately predicting driving postures and positions is crucial for occupant packaging design to accommodate diverse drivers. However, this is challenging due to individual variability and limited access to user data. Digital human modelling (DHM) tools enable posture prediction in virtual environments. This paper presents a case study comparing two driving prediction methods: a statistical prediction method (SPM) and an optimisation prediction method (OPM). Both were evaluated using data from two car models with different seat heights, involving 199 participants whose seat, eye-point, and steering wheel positions were measured. Results showed SPM was more accurate for vertical positioning, whereas OPM for fore-aft positioning. The effectiveness of each method varied by car model, with SPM aligning better in the higher-seated vehicle and OPM performing better in the lower-seated vehicle. These findings highlight the practical, context-specific performance of posture prediction methods. Methodological insights guide the improvement of DHM tool use in occupant packaging.

This study examines how specific collaborative features for independent viewpoint control, collaborative pointing, sketching, and manikin representations support design reviews in a virtual environment. Participants conducted design reviews using the collaborative design tool Gravity Sketch while engaging with these features. Results show that friction situations were minimal, with viewpoint control eliminating the need for perspective adjustments. Collaborative pointing and sketching effectively clarified design modifications, while manikin representations aided discussions on ergonomics. The findings highlight how the evaluated features facilitate communication in remote design reviews.

Industry 5.0 advocates a human-centric approach where humans play a central role in the design and implementation of industrial technologies. Collaborative robots, with their adjustable safety functions, are key enabling technologies in this paradigm, especially for manual assembly tasks. This study investigates initial trust in collaborative robots by examining whether familiarization through virtual reality (VR) influences the applicability of various trust-related factors. Two groups of university engineering students participated: an Online group that evaluated the factors based solely on images and provided general information, and an In-Person group that engaged in a VR interaction with the robot before evaluation. Participants were asked to assess whether they found factors such as safety, usability, competence, predictability, and adaptability applicable to the robots, selecting "Yes", "No", or "I don’t know". In a follow-up question, they were then asked to rate each factor on a 5-point Likert scale. Results indicate that the In-Person group more frequently affirmed the applicability of the factors. Limitations include the use of only positively framed statements and the participants’ prior experience with industrial and collaborative robots. 

När forskare flyr Trumps USA får Europa inte missa chansen att ta emot dem. Nu finns en unik möjlighet för Sverige att bygga ut grundforskningen i en historiskt snabb expansion och rekrytera hundratals amerikanska forskare. Det skriver Oskar MacGregor och Maurice Lamb vid Högskolan i Skövde.

Aims: Recent world events have resulted in companies using remote meeting tools more often in design processes. The shift to remote meeting tools has had a notable impact on collaborative design activities, such as design reviews (DRs). When DRs depend on computer-aided design (CAD) software, the lack of direct support for CAD functionalities in videoconferencing applications introduces novel communication challenges, i.e. friction. This study investigates friction encountered in real world remote DRs when using a combination of standard CAD and videoconferencing applications. The objective was to understand the main sources of friction when carrying out DRs using a combination of CAD and videoconferencing applications.

Methods: At a single Swedish automobile manufacturer, 15 DRs of a fixture component were passively observed. These observations were subjected to a qualitative thematic analysis to identify categories and sources of friction during these DRs. The DRs were carried out using a combination of CATIA CAD software and Microsoft Teams for videoconferencing.

Results: The analysis of the 15 remote DRs identified four recurring friction categories: requesting specific viewpoints, indicating specific elements, expressing design change ideas, and evaluating ergonomics. Each category highlights specific challenges that were observed during the DRs and emerged due to constraints imposed by existing methods and technologies for remote meetings.

Conclusion: This study provides a framework for understanding the current sources of friction in remote DRs using videoconferencing tools. These insights can support the future development of DR software tools, guiding the integration of features that address these friction points. Additionally, the results serve as a guideline for organizations to implement methods that reduce friction in remote DRs and improve DR quality and efficacy.