Virtual Reality Health Education to Prevent Musculoskeletal Disorder and Chronic Low Back Pain in Formal and Informal Caregivers

by Maria Matsangidou (CYENS Centre of Excellence); Theodoros Solomou (University of Cyprus); Cecilie Høegh Langvad (Municipality of Aarhus); Katerina Xinari ("Archangelos Michael" Alzheimer's disease / Dementia nursing home); Ersi Papayianni ("Archangelos Michael" Alzheimer's disease / Dementia nursing home); Constantinos S. Pattichis (University of Cyprus)

Abstract: Formal and informal caregivers are suffering from musculoskeletal disorders and low back pain which develops in practice. Previous research has suggested that health education can help to prevent musculoskeletal disorders and low back pain in caregivers. With the ability to simulate real-world scenarios, healthcare education is experiencing rapid growth in the use of immersive technologies such as Virtual Reality, aiming to enhance lifelong learning for caregivers, with promising results. However, the creation of such technologies has not been well documented, which forces educators to face the same setbacks during this development process. Working closely with 14 medical experts and computer scientists, we co-designed a mobile Virtual Reality application to enhance learning and reduce work attributions in caregivers. Then we evaluated the system with a total of 30 formal and informal caregivers, documenting that Virtual Reality can be an effective solution for lifelong learning. Through this paper, we explain the process and analysis we run to identify how to create an effective Virtual Reality learning system.

Short Papers

Not Facial Expression, nor Fingerprint – Acknowledging Complexity and Context in Emotion Research for Human-Centered Personalization and Adaptation

by Irina Nalis (TU Wien); Julia Neidhardt (TU Wien)

Abstract: While research on emotion has emerged as a crucial area in studying this relationship, the use of classical psychological concepts in human emotion detection and sentiment analysis has been challenged by the cognitive sciences and psychology. This paper argues that the uncritical adoption of concepts that overlook complexity and context of emotions may hinder progress in this field. To overcome this limitation, the theory of constructed emotion is reviewed, which suggests that emotions are not distinct categories but rather dimensions that require dynamic, rather than stactic, contextualized models. By prioritizing digital wellbeing in emotion studies and acknowledging complexity and context, future research can develop more effective models for emotion detection and sentiment analysis. The aim is to provide valuable insights for researchers seeking to advance our understanding of the relationship between technology and wellbeing for human centered-adapation and personalization.

“Who are you?”: Identifying Young Users from a Single Search Query

by Benjamin Bettencourt (Boise State University); Assoumer Redempta Manzi Muneza (Boise State University); Michael Green (Boise State University); Samantha Anguiano (Boise State University); Jerry Alan Fails (Boise State University); Casey Kennington (Boise State University); Katherine Landau Wright (Boise State University); Maria Soledad Pera (Delft University of Technology)

Abstract: As an initial step towards enabling the adaptation of (popular, and widely used) web search environments so that they can better serve children and ease their path towards information discovery, we introduce Recognizing Young Searchers (RYSe). RYSe leverages lexical, syntactical, spelling/punctuation, and vocabulary features that align with the Concrete Operational stage of development (originally identified by Jean Piaget) in an attempt to identify users that are in this stage. The concrete operational stage is commonly associated with children ages 7-11. Findings emerging from our initial empirical exploration using single queries formulated by children and sample queries from adults showcase the feasibility of relying on different cognitive traits inferred from the short text of a single query to distinguish those that are formulated by younger searchers.

Towards Human-Centric Psychomotor Recommender Systems

by Miguel Portaz (UNED); Angeles Manjarrés (UNED); Olga C. Santos (UNED)

Abstract: Recommender Systems have been developed for years to guide the interaction of the users with systems in very diverse domains where information overload exists aimed to help humans in decision making. In order to better support the humans, the more the system knows about the user, the more useful recommendations the user can receive. In this sense, there is a need to explore which are the intrinsic human aspects that should be taken into account in each case when building the user models that provide the personalization. Moreover, there is a need to define and apply methodologies, guidelines and frameworks to develop this kind of systems in order to tackle the challenges of current artificial intelligence applications including issues such as ethics, transparency, explainability and sustainability. For our research, we have chosen the psychomotor domain for three main reasons: i) psychomotor learning deals with many dimensions that have a strong impact on the user such as physical rehabilitation, keeping active when getting older, improving the movement performance in varied situations (novice trying to learn the technique, amateur with experience aimed to improve it, professional looking for the excellence) at the same time that injuries are avoided, ii) recommendations provided are expected to support the acquisition and improvement of motor skills, thus, going beyond just taking into account user preferences as in other domains such as entertainment, and iii) strongly relies on sensing technologies to get information about the movements performed which adds additional challenges from the context. To provide some insights into this problem, in this paper we present our progress when developing the iBAID (intelligent Basket AID) psychomotor system, which aims to recommend the physical activities and movements to perform when training in basketball, either to improve the technique, to recover from an injury or even to keep active when getting older.

Recommender Systems in Continuing Professional Education for Public Transport: Challenges of a Human-Centered Design

by Jose Alejandro Libreros (TU Ilmenau); Cindy Mayas (TU Ilmenau); Matthias Hirth (TU Ilmenau)

Abstract: Continuous training is an essential building block to avoid workforce shortage in the public transport sector in Germany. However, the personnel requirements in this sector are highly diverse, similar to the education history of the different employees. Therefore, more and more specialized continuous training offers arise, which are, on the one hand, more and more personalized but also make it more challenging to find suitable offers for the individual. Specialized recommendation systems for this nice application might be a possible solution. This paper presents current work-in-progress results towards such a system and, in particular, the requirements for the recommender systems from the users' perspective. We conducted guided interviews with industry representatives focusing on the usage-oriented expectations in recommender systems for an online platform for offerings of continuing education in the area of public mobility. The resulting usage requirements form the basis for the concluding literature review of recommender systems in the special application domain. The results show that especially the challenges of small communities with confidential content and multiple profiles are not sufficiently addressed in the development of recommender systems, such that existing solutions are not applicable in this niche area.

Children on ChatGPT Readability in an Educational Context: Myth or Opportunity?

by Emiliana Murgia (Università di Genova); Maria Soledad Pera (TU Delft);  Monica Landoni (USI); Theo Huibers (University of Twente)

Abstract: In this work, we present the results of a preliminary exploration aiming to understand whether the use of ChatGPT in an educational context can be an asset to meet the specific needs of the students. In particular, we focus on the possibility to adapt the responses to online inquiries related to the primary school curriculum to meet the expectations of readers with different literacy levels. The analysis of feedback elicited from children (9- to 10-year-olds) in three 4th grade classrooms indicates that ChatGPT can adapt the responses to 4th grade level. However, it needs improvement to reach the right level of readability. Outcomes from this work can inspire future research directions involving technologies like ChatGPT in the pursuit of adapting learning paths to suit a broad range of students with varied cognitive skills. The potential of such tools to support teachers in their effort to adapt to individual learning needs is still to be fully exploited.