Although Augmented Reality (AR) technology has entered many market and knowledge domains such as games and leisure activities, it remains rather limited in digital heritage. After studying the potentiality of using modern AR elements in a museum context, this paper proposes the use of additional game and educational elements in the core AR application in order to enhance the overall on-the-spot museum visitor’s experience. An agile AR application design methodology was followed by taking into account the needs of small-to-medium sized real-world museums. Moreover, a heuristic evaluation protocol was applied by a group of experts in order to test the proof-of-concept AR application, in which some novel elements were proposed such as the AR quiz game. The main findings indicate that enhanced AR experiences in museum settings can make a nice fit with the user environment, physical and perceptual abilities, known metaphors, and user position and motion in 3D space. Moreover, AR services can be provided under a minimum distraction and physical effort. As a conclusion, AR technologies are mature enough to be standardized for museum usage, while the audience seems to be ready to take advantage of the related enhanced museum experiences to maximize both user satisfaction and learning outcomes.

Stress can be considered a mental/physiological reaction in conditions of high discomfort and challenging situations. The levels of stress can be reflected in both the physiological responses and speech signals of a person. Therefore the study of the fusion of the two modalities is of great interest. For this cause, public datasets are necessary so that the different proposed solutions can be comparable. In this work, a publicly available multimodal dataset for stress detection is introduced, including physiological signals and speech cues data. The physiological signals include electrocardiograph (ECG), respiration (RSP), and inertial measurement unit (IMU) sensors equipped in a smart vest. A data collection protocol was introduced to receive physiological and audio data based on alterations between well-known stressors and relaxation moments. Five subjects participated in the data collection, where both their physiological and audio signals were recorded by utilizing the developed smart vest and audio recording application. In addition, an analysis of the data and a decision-level fusion scheme is proposed. The analysis of physiological signals includes a massive feature extraction along with various fusion and feature selection methods. The audio analysis comprises a state-of-the-art feature extraction fed to a classifier to predict stress levels. Results from the analysis of audio and physiological signals are fused at a decision level for the final stress level detection, utilizing a machine learning algorithm. The whole framework was also tested in a real-life pilot scenario of disaster management, where users were acting as first responders while their stress was monitored in real time.

Emotion recognition is a key attribute for realizing advances in human–computer interaction, especially when using non-intrusive physiological sensors, such as electroencephalograph (EEG) and electrocardiograph. Although functional connectivity of EEG has been utilized for emotion recognition, the graph theory analysis of EEG connectivity patterns has not been adequately explored. The exploitation of brain network characteristics could provide valuable information regarding emotions, while the combination of EEG and peripheral physiological signals can reveal correlation patterns of human internal state. In this work, a graph theoretical analysis of EEG functional connectivity patterns along with fusion between EEG and peripheral physiological signals for emotion recognition has been proposed. After extracting functional connectivity from EEG signals, both global and local graph theory features are extracted. Those features are concatenated with statistical features from peripheral physiological signals and fed to different classifiers and a Convolutional Neural Network (CNN) for emotion recognition. The average accuracy on the DEAP dataset using CNN was 55.62% and 57.38% for subject-independent valence and arousal classification, respectively, and 83.94% and 83.87% for subject-dependent classification. Those scores went up to 75.44% and 78.77% for subject-independent classification and 88.27% and 90.84% for subject-dependent classification using a feature selection algorithm, exceeding the current state-of-the-art results.

Demographic change confronts us with an ever-increasing number of elderly people who face isolation and socialization issues. Background: The main challenge of this study is to inject emotional and aesthetic aspects into the design process of a virtual reality (VR) social space for the elderly. In this context, we asked architects and artists to improve the perception elderly people have of their way of communicating with others. Artists, in collaboration with computer engineers, designed experiences that evoke positive cognitive and emotional feelings and memories by following design trends and aesthetic values likely to be appreciated by older people, which were integrated in VR. Methods: We approached our goal by implementing an innovative art-driven methodology, using a plethora of technologies and methods, such as VR, artificial intelligence algorithms, visual analysis, and 3D mapping, in order to make design decisions based on a detailed understanding of the users’ preferences and collective behavior. Results: A so-called virtual village “Cap de Ballon” was co-created, having a public space inspired by the villages of Santorini and Meteora and a private space inspired by the 3D scanning of an elderly person’s apartment. Conclusions: The overall concept of the VR village‘s utility, design, and interior design were appreciated by the end users and the concept was evaluated as original and stimulating for creativity.

The intersection of serious games, advanced technologies, and culture offers new paths for fostering social inclusion and sustainability. This position paper presents i-Game, an EU project dedicated to transforming the relationship between cultural heritage and the gaming industry. By creating an open-source platform for inclusive game development, i-Game integrates tools such as Explainable AI (XAI) and virtual reality to enable diverse stakeholders—including underrepresented groups—to co-create games that address societal and cultural challenges. Through pilot projects in museums and the textile industry, i-Game explores the potential of gamification to enhance learning, creativity, and collaboration while advancing sustainable practices. The project highlights how interdisciplinary methodologies and ethical-design principles can reshape the cultural heritage landscape, democratizing access to innovative tools and fostering a deeper connection between technology and heritage.