Högskolan i Skövde

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. 

The education of students to become competent user experience designers is a delicate matter as students need to obtain a multitude of knowledge, skills, and judgmental abilities. In this paper, our effort to manage this multiplicity in a bachelor’s program in user experience design is shared along with our experiences and teaching practices influenced by theories of situated and embodied cognition together with reflective teaching. The program was followed up through interviews with eight alumni and a company representative that employs user experience designers. The results show that the program overall works well, although some of the identified issues need to be addressed in the future. The interpretation is that our program curricula and teaching practices are fruitful, which hopefully can contribute to thoughts and discussions for other teachers in the field of user experience design and human-computer interaction.

The ANEMONE is a methodological approach for user experience (UX) evaluation of action and intention recognition in human-robot interaction that has activity theory as its theoretical lens in combination with the seven stages of action model and UX evaluation methodology. ANEMONE has been applied in a case where a prototype has been evaluated. The prototype was a workstation in assembly in manufacturing consisting of a collaborative robot, a pallet, a tablet, and a workbench, where one operator is working in the same physical space as one robot. The purpose of this paper is to provide guidance on how to use ANEMONE, with a particular focus on the data analysis part, through describing a real example together with lessons learned and recommendations.

For social robots, like in all other digitally interactive systems, products, services, and devices, positive user experience (UX) is necessary in order to achieve the intended benefits and societal relevance of human–robot interaction (HRI). The experiences that humans have when interacting with robots have the power to enable, or disable, the robots’ acceptance rate and utilization in society. For a commercial robot product, it is the achieved UX in the natural context when fulfilling its intended purpose that will determine its success. The increased number of socially interactive robots in human environments and their level of participation in everyday activities obviously highlights the importance of systematically evaluating the quality of the interaction from a human-centered perspective. There is also a need for robot developers to acquire knowledge about proper UX evaluation, both in theory and in practice. In this chapter we are asking: What is UX evaluation? Why should UX evaluation be performed? When is it appropriate to conduct a UX evaluation? How could a UX evaluation be carried out? Where could UX evaluation take place? Who should perform the UX evaluation and for whom? The aim is to briefly answer these questions in the context of doing UX evaluation in HRI, highlighting evaluation processes and methods that have methodological validity and reliability as well as practical applicability. We argue that each specific HRI project needs to take the UX perspective into account during the whole development process. We suggest that a more diverse use of methods in HRI will benefit the field, and the future users of social robots will benefit even more.

The coexistence of robots and humans in shared physical and social spaces is expected toincrease. A key enabler of high-quality interaction is a mutual understanding of each other’s actionsand intentions. In this paper, we motivate and present a systematic user experience (UX) evaluationframework of action and intention recognition between humans and robots from a UX perspective,because there is an identified lack of this kind of evaluation methodology. The evaluationframework is packaged into a methodological approach called ANEMONE (action and intentionrecognition in human robot interaction). ANEMONE has its foundation in cultural-historicalactivity theory (AT) as the theoretical lens, the seven stages of action model, and user experience(UX) evaluation methodology, which together are useful in motivating and framing the workpresented in this paper. The proposed methodological approach of ANEMONE provides guidanceon how to measure, assess, and evaluate the mutual recognition of actions and intentions betweenhumans and robots for investigators of UX evaluation. The paper ends with a discussion, addressesfuture work, and some concluding remarks.

Affective touch has a fundamental role in human development, social bonding, and for providing emotional support in interpersonal relationships. We present, what is to our knowledge, the first HRI study of tactile conveyance of both positive and negative emotions (affective touch) on the Nao robot, and based on an experimental set-up from a study of human–human tactile communication. In the present work, participants conveyed eight emotions to a small humanoid robot via touch. We found that female participants conveyed emotions for a longer time, using more varied interaction and touching more regions on the robot’s body, compared to male participants. Several differences between emotions were found such that emotions could be classified by the valence of the emotion conveyed, by combining touch amount and duration. Overall, these results show high agreement with those reported for human–human affective tactile communication and could also have impact on the design and placement of tactile sensors on humanoid robots.