Högskolan i Skövde

Background Mental illness is prevalent worldwide, creating a demand for Emergency Medical Service (EMS) assessments in mental illness, yet research on the epidemiology of patients with mental illness in the EMS is lacking in Sweden. This study aims to describe the patients presenting with symptoms of mental illness in the EMS and how they are assessed in the prehospital setting.

Method A retrospective observational study was conducted to identify patients assessed for symptoms of mental illness in the EMS in 2023. A total of 1,304 records met the inclusion criteria and were included in the study: [1] assessed in the EMS due to symptoms of mental illness and [2] over 13 years old. The data were analysed using IBM SPSS Statistic 28.

Results More females (54.3%) than men (45.7%) were assessed for mental illness (p=<0.01). The median age was 39 years, with an interquartile range (IQR) of 32 years (p=<0.01) and a total range of 13–91. Most patients were assessed once, with a range of 1 to 37 times. The initial priority of the patients was mainly Priority 1 (45.6%) or Priority 2 (49.9%). However, this shifted after the EMS assessment where most patients either recieved a lower priority or No priority [due to not being transported] (39.7%). The most common triage colour was Orange (21.4%), indicating the need for acute care, but four out of ten patients did not recieve a triage color (40.4%). The most frequent patients assessed by the EMS were suicide threats/attempts (45.2%) and intoxications (48.8%) with intoxication cases most likely to be hospitalised. The length of the stay in the hospital varied from 0 to 67 days but most patients were discharged within 24 h (6.8%) or admitted for 24 h (6.4%).

Conclusion Patients with mental illness are frequently assessed in the EMS, primarily for suicide threats/attempts and intoxication. However, few are admitted to the hospital, and many are not triaged, suggesting difficulties in referring patients with mental illness to the right level of care. The result may inform future studies assessing patients with mental illness in the EMS.

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.

Large knowledge gaps exist regarding how enabling technologies, such as robotics, automation, and AI, impact the digital work environment in the ongoing industrial revolution. While these technologies must align with work tasks, they also have the potential to enhance meaningfulness at work by fulfilling human needs for competence, autonomy, and self-realization. However, the development of laboratory automation has mainly been technology-driven, underscoring the need for a human-centered approach here called Laboratory 5.0. This paper presents an empirical UX evaluation of a semi-automated robot used in inhalator testing by five chemists in a pharmaceutical lab. Data collection included think-aloud protocols, questionnaires, observations, and interviews, analyzed through triangulation. The findings revealed that while the robot streamlined tasks and increased flexibility, it also introduced physical and cognitive work environment challenges, such as ergonomic strain, cognitive load, unclear process feedback, and workflow interruptions. These insights will guide the redesign and implementation of a mobile lab robot, addressing user experience and interaction challenges in the laboratory context. By improving UX in human-robot interaction, this research aims to contribute to a more sustainable, meaningful and engaging digital work environment in Laboratory 5.0, which will benefit chemists and workers in similar domains.

In Industry 4.0 and 5.0, humans and robots share physical and social spaces, making sustainable workplaces essential. While mobile, flexible, and collaborative robots offer new possibilities, challenges remain. Effective human-robot interaction relies on mutual recognition of actions and intentions for efficiency, safety, and smooth collaboration. Awareness of user experience (UX) is growing, as it influences work satisfaction, engagement, and well-being. This paper identifies UX issues when mobile robots are introduced on the shop floor alongside operators and truck drivers. The study, conducted at a large manufacturing plant, examined mobile robots delivering heavy parts to the assembly line and navigating a busy warehouse corridor and production area. Two months after field testing, a post-evaluation took place, with data collected from eight participants, four operators and four truck drivers, who had frequently interacted with the robots. Participants completed a questionnaire and an interview. Key findings show: a) differences between operators and truck drivers, b) higher UX assessments for operators, c) more and greater challenges for truck drivers, and d) neither group seeing major personal benefits but recognizing advantages for the company. Both physical and cognitive work environment problems were identified.

Increasing age is often associated with a decline in the sense of health, well-being and quality of life, which can contribute to social isolation and a sense of loneliness. Demographic changes and the transfer of care from institution to home raise the need for innovative solutions to preserve and promote well-being and quality of life. There is a growing interest in using robots in care for older adults. Despite its potential to improve well-being, using robots in care for older adults remains limited. This paper adheres to the importance of having a user-centred approach as the focal point. The purpose is to explore social robots as a means to enhance the quality of life, and the research question is: What values can a social robot fill in everyday life for older adults? Qualitative data was collected in a workshop with eight older adults. The workshop consisted of two phases: 1) interaction with Pepper and 2) responding to a questionnaire followed by reflection and discussion individually and group-wise. Thematic analysis was conducted, which generated three overall themes: variation in the older adults’ first encounter experiences with Pepper; the participants’ main challenges in daily life at home; the potential value of having a social robot at home; and aspects affecting experienced value. The insights from the workshop resulted in the research project Social Robots in Home Environments for Older Persons’ Quality of Life - Needs, Opportunities and Obstacles (RO-LIV). 

Socially Assistive Robots (SAR) has been suggested as an important technology in the shift of care from institution to home environments, and has been shown to nr effective in addressing loneliness and social isolation among older adults (Lee et al. 2023., Lorenz et al., 2016, Shishehgar et al., 2018). In a newly started research project RO-LIV, we employ a user experience design approach, involving older adults as co-designers and engaged actors, in order to identify needs, solutions, and obstacles for integrating socially assistive robots into older adults' homes. The research is organized into three work packages: Needs Analysis, Current Situation Analysis, and Conditions and Obstacles for Integration into the Home Environments. The expected results include a road map for the integration of socially assistive robots into older adults' homes, informed by a nuanced understanding of user needs and preferences. Overall, we emphasize the importance of adopting a user-centered approach in human-robot interaction research, particularly when designing solutions for older adults. By involving older adults in the design process and addressing their diverse needs, researchers can develop robotic systems that are address real user needs, are socially acceptable, and has an increased potential for adoption and impact on quality of life.

Product returns are not only costly for e-tailers, but the unnecessary transports also impact the environment. Consequently, online retailers have started to formulate policies to reduce the number of returns. Determining when and how to act is, however, a delicate matter, since a too harsh approach may lead to not only the order being cancelled, but also the customer leaving the business. Being able to accurately predict which orders that will lead to a return would be a strong tool, guiding which actions to be taken. This paper addresses the problem of data-driven product return prediction, by conducting a case study using a large real-world data set. The main results are that well-calibrated probabilistic predictors are essential for providing predictions with high precision and reasonable recall. This implies that utilizing calibrated models to predict some instances, while rejecting to predict others can be recommended. In practice, this would make it possible for a decision-maker to only act upon a subset of all predicted returns, where the risk of a return is very high.

The industrial evolutions require robots to be able to share physical and social space with humans in such a way that interaction and coexistence arepositively experienced by human workers. A prerequisite is the possibility for the human and the robot to mutually perceive, interpret and act on each other's actions and intentions. To achieve this, strategies for human-robot interaction are needed that are adapted to operators’ needs and characteristics in the industrial contexts. In this paper, we aim to present various taxonomies of levels of automation, humanrobot interaction, and human-robot collaboration suggested for the envisioned factories of the future. Based on this foundation, we propose a compass direction for continued research efforts which both zooms in and zooms out on how to develop applicable human-robot interaction strategies that are worker-centric in order to obtain effective, efficient, safe, sustainable, and pleasant human-robot collaboration and coexistence.

The industrial transition to Industrie 4.0 and subsequently Industrie 5.0 requires robots to be able to share physical and social space with humans in such a way that interaction and coexistence are positively experienced by the humans and where it is possible for the human and the robot to mutually perceive, interpret and act on each other's actions and intentions. To achieve this, strategies for humanrobot interaction are needed that are adapted to operators’ needs and characteristics in an industrial context, i.e., Operator 5.0. This paper presents a research design for the development of a framework for human-robot interaction strategies based on ANEMONE, which is an evaluation framework based on activity theory, the seven stages of action model, and user experience (UX) evaluation methodology. At two companies, ANEMONE is applied in two concrete use cases, collaborative kitting and mobile robot platforms for chemical laboratory assignments. The proposed research approach consists of 1) evaluations of existing demonstrators, 2) development of preliminary strategies that are implemented, 3) re-evaluations and 4) cross-analysis of results to produce an interaction strategy framework. The theoretically and empirically underpinned framework-to-be is expected to, in the long run, contribute to a sustainable work environment for Operator 5.0.

Customer churn is one of the most challenging problems for digital retailers. With significantly higher costs for acquiring new customers than retaining existing ones, knowledge about which customers are likely to churn becomes essential. This paper reports a case study where a data-driven approach to churn prediction is used for predicting churners and gaining insights about the problem domain. The real-world data set used contains approximately 200 000 customers, describing each customer using more than 50 features. In the pre-processing, exploration, modeling and analysis, attributes related to recency, frequency, and monetary concepts are identified and utilized. In addition, correlations and feature importance are used to discover and understand churn indicators. One important finding is that the churn rate highly depends on the number of previous purchases. In the segment consisting of customers with only one previous purchase, more than 75% will churn, i.e., not make another purchase in the coming year. For customers with at least four previous purchases, the corresponding churn rate is around 25%. Further analysis shows that churning customers in general, and as expected, make smaller purchases and visit the online store less often. In the experimentation, three modeling techniques are evaluated, and the results show that, in particular, Gradient Boosting models can predict churners with relatively high accuracy while obtaining a good balance between precision and recall.