Cognitive Stimulation Therapy (CST) is an evidence-based intervention that involves group or individual sessions demonstrating cognitive benefits in Mild Cognitive Impairment. Recent research suggests that social robots can effectively assist therapists during cognitive stimulation sessions for people with MCI. Building on these findings, we conducted an experimental study to evaluate the impact of a Socially Assistive Robot, on engagement and emotional responses during cognitive interventions for a group of elderly people. Each session was video-recorded for subsequent analysis. The findings suggest that the use of a Social Robot as a mediating tool in CST interventions is associated with high levels of participant engagement and predominantly positive-valenced emotional responses, as detected by both human and automatic evaluations.

This paper presents the development of CArEN (Conversational AgEnt supporting Narrative medicine) that integrates a text-based emotional recognition module to personalize therapeutic pathways in the context of Narrative-Based Medicine (NBM).NBM combines traditional medicine, therapies, symptom monitoring, and vital parameters detection with a conversation-based approach that allows considering not only physical well-being but also the psychosocial and emotional impact of illness on the patient's life.A study was carried out to evaluate the models' effectiveness in real-world contexts and collect user feedback on the conversational agent's performance and empathic support.The results demonstrate good accuracy in emotion recognition and positive user feedback, highlighting the conversational agent's potential as an effective means of supporting narrative medicine techniques.

Childhood obesity and overweight are concerning issues worldwide with significant health and social implications, making it necessary to implement prevention and intervention policies. Engaging children through creative and playful methods is an effective approach to motivating behavior change. Social robots have emerged as promising tools in education and persuasive technology, due to their interactive and engaging nature. This paper presents a study examining the potential of a social robot acting as a nutrition coach for children, by comparing interactions with a social robot versus a chatbot. Results suggest that, in the short term, the social robot was perceived as more persuasive, increasing participants’ intention to adopt healthier eating behavior. These findings offer insights for the development of personalized nutrition coaching for children.

Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing the visitors’ experience, whether onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access, both on-site and online, to CH assets. Recent advances in ICT enhance visitors’ access to online collections as well as their CH experience onsite, bringing even wider audiences than those who visit the physical museums. The range of visualization devices – from tiny smartwatch screens, through large situated public displays, to the latest generation of immersive Head-Mounted Displays – together with the increasing availability of real-time 3D rendering technologies for online and mobile devices and, recently, Internet of Things (IoT) approaches, Social Robotics, requires exploring how they can be applied successfully in CH. Following the successful workshops at AVI 2016, AVI 2018, AVI 2020, AVI 2022 and AVI 2024 and a large number of recent events and projects focusing on CH, the goal of the workshop is to bring together researchers and practitioners interested in presenting and discussing the potential of state of the art advanced visual interfaces in enhancing our daily cultural heritage experience. This year the workshop theme will be: AVI for accessibility in CH.

Engaging in regular exercise offers numerous mental and physical health benefits. Many people, after the Covid-19 pandemic, started to work out at home. However, performing exercises incorrectly can result in injuries. For this reason, technology can be an effective tool for encouraging physical activity at home, and social robots have emerged as a potential solution for providing fitness training in a safe and engaging manner. A physical robot is often perceived as more socially attractive compared to virtual agents. Despite the growing popularity and existing literature on social robotics-based fitness solutions, there is a lack of research examining the accuracy of these systems and interventions at home during physical exercise recognition. In this paper, we propose a novel approach utilizing the Ubtech alpha mini social robot, which possesses motor capabilities to demonstrate exercises. We endowed the robot with the capability to recognize exercise correctness and motivate users to engage in physical activities. In this project, first of all, a new dataset of physical exercises was created, including 1,500 videos of physical exercises. Then, a new approach to recognize physical exercises from image sequences was presented. This method was implemented in a personal social robot to recognize physical exercises using a deep learning model. The results of the study validate the effectiveness of the system in recognizing physical exercises through the camera onboard a personal social robot.

Childhood overweight and obesity represent an alarming problem worldwide with strong consequences both at health and social levels. Learning based on storytelling is an effective approach to enhancing children's knowledge and correct behaviors, especially when it is conveyed by engaging novel technologies. Among these technologies, social robots have a special appeal for educational purposes. The paper presents a pilot project in which the social robot Pepper is used as an educational storyteller in the context of nutrition. To investigate the appropriateness of the approach, a preliminary study was performed to compare the effects of Pepper vs a human storyteller. Results are interesting in terms of engagement, user experience, trust, and learning of relevant concepts, suggesting that the proposed approach can be effective in this domain.

The automatic monitoring and assessment of the engagement level of learners in distance education may help in understanding problems and providing personalized support during the learning process. This article presents a research aiming to investigate how student engagement level can be assessed from facial behavior and proposes a model based on Long Short-Term Memory (LSTM) networks to predict the level of engagement from facial action units, gaze, and head poses. The dataset used to learn the model is the one of the EmotiW 2019 challenge datasets. In order to test its performance in learning contexts, an experiment, involving students attending an online lecture, was performed. The aim of the study was to compare the self-evaluation of the engagement perceived by the students with the one assessed by the model. During the experiment we collected videos of students behavior and, at the end of each session, we asked students to answer a questionnaire for assessing their perceived engagement. Then, the collected videos were analyzed automatically with a software that implements the model and provides an interface for the visual analysis of the model outcome. Results show that, globally, engagement prediction from students' facial behavior was weakly correlated to their subjective answers. However, when considering only the emotional dimension of engagement, this correlation is stronger and the analysis of facial action units and head pose (facial movements) are positively correlated with it, while there is an inverse correlation with the gaze, meaning that the more the student's feels engaged the less are the gaze movements.

People use various nonverbal communicative channels to convey emotions, among which facial expressions are considered the most important ones. Thus, automatic Facial Expression Recognition (FER) is a fundamental task to increase the perceptive skills of computers, especially in human-computer interaction. Like humans, state-of-art FER systems are able to recognize emotions from the entire face of a person. However, the COVID-19 pandemic has imposed a massive use of face masks that help in preventing infection but may hamper social communication and make the recognition of facial expressions a very challenging task due to facial occlusion. In this paper we propose a FER system capable to recognize emotions from masked faces. The system checks for the presence of a mask on the face image and, in case of mask detection, it extracts the eyes region and recognizes the emotion only considering that portion of the face. The effectiveness of the developed FER system was tested in recognizing emotions and their valence only from the eyes region and comparing the results when considering the entire face. As it was expected, emotions that are related mainly to the mouth region (e.g., disgust) are barely recognized, while positive emotions are better identified by considering only the eyes region. Moreover, we compared the results of our FER system to the human annotation of emotions on masked faces. We found out that the FER system outperforms the human annotation, thus showing that the model is able to learn proper features for each emotion leveraging only the eyes region.

AVI-CH is the 14th workshop in the series of PATCH workshops, since 2007 and the 4th in a row at AVI. It is the meeting place for researchers and practitioners focusing on the application of advanced information and communication technology (ICT) in cultural heritage with a specific focus on user interfaces, visualization and interaction. This year, eight papers were submitted by researchers from Greece, Italy and Israel. All were accepted.

, that is, the stratification of all the acoustic sources that, often unconsciously, are heard. This paper presents a framework for archiving, browsing, and accessing soundscapes, either remotely or on-site. The framework is based on two main components: a web-based interface to upload and search the recordings of an acoustic environment, enriched by in- formation about geolocation, timing, and context of the recording; and a mobile app to browse and listen to the recordings, using an interactive map or GPS information. To populate the archive, we launched two crowdsourcing initiatives. An initial experiment examined the city of Padua's soundscape through the participation of a group of undergraduate students. A broader experiment, which was proposed to all people in Italy, aimed at tracking how the nationwide COVID-19 lockdown was dramatically changing the soundscape of the entire country.

AVI2CH is a meeting place for researchers and practitioners focusing on the application of advanced information and communication technology in cultural heritage (CH) with a specific focus on user interfaces, visualization and interaction. It builds on a series of PATCH workshops, since 2007 including three at AVI and also a series of European workshops on cultural informatics. Eleven papers range from novel interfaces in museums to wider community engagement; all share a common mission to ensure that the latest digital technology helps preserve the past in ways that enrich the lives of current and future generations

Recent studies show that children affected by Autism Spectrum Disorder (ASD) are more exposed to pathologies related to obesity and lack of movement. Moreover, they are approximately twice as likely to die from drowning than neurotypical ones. Therefore, acquiring good water safety skills is of extreme importance and, at the same time, aquatic activities are a valid opportunity to do some physical activity and reduce sedentary behaviors. "iBall to Swim is a serious game, based on IoT, that through a playful approach allows children with ASD to do activities in an aquatic environment, developing and improving motor skills. The system is made of a swimming ball augmented with lighting, a wetsuit with a heartbeat monitor and wireless bone conduction headphones. A mobile application is used to integrate these components and to measure and monitor the child's performance. To test whether the technology contributed to improve children's motor skills, we performed a test with eleven children with ASD. Their improvement in motor skills has been studied during a water training phase both with the help of the serious game and without. Results show that there was a general improvement in their performance and children were keeping swimming autonomously and for a longer distance when they were stimulated by the game. Furthermore, the children reported enjoyment and the parents asked whether the game could be used routinely with their children. These encouraging findings suggest that "iBall to Swim is a promising way to enhance the learning of the basic notions of swimming and it can be considered a valid tool to help to improve ASD children's health and wellbeing.

Humans use a wide range of non-verbal social signals while communicating with each other. Gestures are part of these signals and social robots should be able to recognize them for responding appropriately during a dialogue and being more socially believable. Gesture recognition is a hot topic in Computer Vision since a long time. This is particularly due to the fact that the segmentation of foreground objects from a cluttered background is a challenging problem, especially if it has to be performed in real-time. In this paper, we propose a vision-based framework for making social robots capable of recognizing and responding in real-time to a specific greeting gesture, namely the hand waving. The framework is based on a Convolutional Neural Network model trained to recognize hand gestures. Preliminary experiments in a lab setup with the social robot Pepper indicate that the robot correctly recognizes the wave gesture 90% of the times and answers appropriately in real-time by waving itself, thus increasing its social believability.

In this paper we investigate the use of a social robot as an interface to a serious game aiming to train kids in how to recycle materials correctly. Serious games are mostly used to induce motivations and engagement in users and support knowledge transfer during playing. They are especially effective when the goal of the game concerns behavior change. In addition, social robots have been used effectively in educational settings to engage children in the learning process. Following this trend, we designed a serious game in which the social robot Pepper plays with a child to teach him to correctly recycle the materials. To endow the robot with the capability of detecting and classifying the waste material we developed an image recognition module based on a Convolutional Neural Network. Preliminary experimental results show that the implementation of a serious game about recycling into the Pepper robot improves its social behavior. The use of real objects as waste items during the game turns out to be a successful approach not only for perceived learning effectiveness but also for engagement of the children.

Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing visitors' experience, either onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access to CH assets. Advances in ICT ease visitors to access collections online and better experience CH onsite. The range of visualization devices - from tiny smart watch screens and wall-size large situated public displays to the latest generation of immersive head-mounted displays - together with the increasing availability of real-time 3D rendering technologies for online and mobile devices and, recently, Internet of Things (IoT) approaches, require exploring how they can be applied successfully in CH. Following the successful workshop at AVI 2016 and the large numbers of recent events and projects focusing on CH and, considering that 2018 has been declared the European Year of Cultural Heritage, the goal of the workshop is to bring together researchers and practitioners interested in presenting and discussing the potential use of state-of-the-art advanced visual interfaces in enhancing our daily CH experience.

This paper describes the first prototype of a gaze-based system designed for providing interactively information about dresses shown, on physical mannequins, in a shop window. Using the system the user may look at available sizes, colors, price and similar products. Due to the nature of such a system, the interaction must be touchless and natural. The developed solution uses Microsoft Kinect 2 as a device to achieve a natural gaze-based pointing approach. Results show that users evaluate positively the approach and felt positively engaged during the interaction.

Cultural heritage traditionally draws a lot of research attention when it comes to exploring the potential benefits from application of novel technology in realistic settings. The domain is rich of physical as well as virtual sites objects and infinite information about them. Hence, it is only natural that whenever new technology appears, it is experimented in cultural heritage -- from early dialog systems to state of the art Humanoid robots, eye trackers, virtual/augmented reality devices, and the Internet of Things (IoT). The AVI-CH workshop nicely demonstrate this with the diversity of topics presented by the papers accepted to the workshop.

The 4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE) is taking place in Boston on September 16th, 2016 in conjunction with the ACM RecSys 2016 conference. The workshop focuses on the acquisition and usage of emotions and personality as user-centric aspects of personalization.

The availability on the Internet of huge amounts of blog posts, messages and comments allows to study the attitude of people on various topics. Sentiment Analysis, Opinion Mining and Emotion Analysis denote the area of research in Computer Science aimed at studying, analyzing and classifying text documents based on the underlying opinions expressed by their authors on various topics. While this is a tough task, because it is related to psychological aspects that are not always immediately evident in the lexical and syntactical aspects of the sentences, its importance may be paramount for several applications such as market analysis, political polls, etc. Fundamental pre-processing techniques for this task come from the area of Natural Language Processing, which may pose additional problems when the language of interest is different than English, and thus less (or less reliable) resources are available to extract the needed data from the text. This paper studies the performance of Sentiment Analysis, seen as a Text Categorization task, depending on the use of different classifiers and different features. While the approach is general, we focus on texts in Italian. The outcomes suggest which experimental settings can be most profitably used in this landscape, and show that significantly good results can be obtained.

Smart home environments should proactively support users in their activities, anticipating their needs according to their preferences. Understanding what the user is doing in the environment is important for adapting the environment's behavior, as well as for identifying situations that could be problematic for the user. Enabling the environment to exploit models of the user's most common behaviors is an important step toward this objective. In particular, models of the daily routines of a user can be exploited not only for predicting his/her needs, but also for comparing the actual situation at a given moment with the expected one, in order to detect anomalies in his/her behavior. While manually setting up process models in business and factory environments may be cost-effective, building models of the processes involved in people's everyday life is infeasible. This fact fully justifies the interest of the Ambient Intelligence community in automatically learning such models from examples of actual behavior. Incremental adaptation of the models and the ability to express/learn complex conditions on the involved tasks are also desirable. This article describes how process mining can be used for learning users’ daily routines from a dataset of annotated sensor data. The solution that we propose relies on a First-Order Logic learning approach. Indeed, First-Order Logic provides a single, comprehensive and powerful framework for supporting all the previously mentioned features. Our experiments, performed both on a proprietary toy dataset and on publicly available real-world ones, indicate that this approach is efficient and effective for learning and modeling daily routines in Smart Home Environments.

The current tools to create OWL-S annotations have been designed starting from the knowledge engineer’s point of view. Unfortunately, the formalisms underlying Semantic Web languages are often incomprehensible to the developers of Web services. To bridge this gap, it is desirable that developers are provided with suitable tools that do not necessarily require knowledge of these languages in order to create annotations on Web services. With reference to some characteristics of the involved technologies, this work addresses these issues, proposing guidelines that can improve the annotation activity of Web service developers. Following these guidelines, we also designed a tool that allows a Web service developer to annotate Web services without requiring him to have a deep knowledge of Semantic Web languages. A prototype of such a tool is presented and discussed in this paper.

The problem of implementing socially intelligent agents has been widely investigated in the field of both Embodied Conversational Agents (ECAs) and Social Robots that have the advantage of offering to people the possibility to relate with computer media at a social level. We focus our study on the recognition of the social response of users to embodied agents in the context of ambient intelligence. In this paper we describe how we extended a model for recognizing the social attitude in natural conversation from text by adding two additional knowledge sources: speech and gestures.

As far as interaction is concerned Ambient Intelligence (AmI) research emphasizes the need of natural and friendly interfaces for accessing services provided by the environment. In this paper we present the result of an experimental study aiming at understanding whether Embodied Conversational Agents (ECAs) and Social Robots may improve the naturalness and effectiveness of interaction by playing different roles when acting as interface between users and smart environment services. Results obtained so far show that ECAs seem to have a better evaluation than robots for information related tasks. On the other side, Social Robots are preferred for welcoming people and for guiding them in the smart environment, due to their possibility to move and to the perceived sense of presence. Moreover, the robot seems to elicit a more positive evaluation in terms of user experience.

This paper presents the development of a presentation team that shows, explains and provides useful advices to children in the domain of "healthy nutrition". Agents of the team are endowed with different roles and personality traits so as to explain domain concepts from different viewpoints. In order to develop a system independent from the domain and the bodies of the agents, explanations and pieces of advice are generated and not statically scripted. The presentation plan is distributed to the various team actors and it is adapted to the characteristics of the interlocutor. To test the system and to evaluate the efficacy of the team in this domain we used a family of characters already present in the cartoon world: the Smurfies.

When visiting cities as tourists, most of the times people do not make very detailed plans and, when choosing where to go and what to seem they tend to select the area with the major number of interesting facilities. Therefore, it would be useful to support the user choice with contextual information presentation, information clustering and comparative explanations of places of potential interest in a given area. In this paper we illustrate how MyMap, a mobile recommender system in the Tourism domain, generates comparative descriptions to support users in making decisions about what to see, among relevant objects of interest.

In this paper we propose a model for the use of a social robot as mediator between the user and a smart environment. Since the speech is considered one of the more natural and immediate input channel in human-robot interaction we discuss the importance of recognizing both the linguistic content of the spoken sentence and the tone valence of the user voice in order to infer properly the user’s communication aim during the interaction.

In this paper, we present the first results of a research aiming at developing an intelligent agent able to interact with users in public spaces through a touch screen or a personal device. The agent communication is adapted to the situation at both content and presentation levels, by generating an appropriate combination of verbal and non-verbal agent behaviours.

We illustrate how a formal model of interaction can be employed to generate documentation on how to use an application, in the form of an Animated Agent. The formal model is XDM, an extension of Coloured Petri Nets that enables representing user-adapted interfaces, simulating their behaviour and making pre-empirical usability evaluations. XDM-Agent is a personality-rich animated character that uses this formal model to illustrate the role of interface objects and to explain how tasks may be performed; its behaviour is programmed by a schema-based planning followed by a surface generation, in which verbal and non-verbal acts are combined appropriately, the agent's 'personality' may be adapted to the user characteristics.

Article Free Access Share on Generating mixed-initiative hypertexts: a reactive approach Author: Berardina De Carolis Intelligent Interfaces Research Group, Department of Informatics, University of Bari, Via Orabona, 4 Bari, 70126 ITALY Intelligent Interfaces Research Group, Department of Informatics, University of Bari, Via Orabona, 4 Bari, 70126 ITALYView Profile Authors Info & Claims IUI '99: Proceedings of the 4th international conference on Intelligent user interfacesDecember 1998 Pages 71–78https://doi.org/10.1145/291080.291093Published:01 December 1998Publication History 3citation239DownloadsMetricsTotal Citations3Total Downloads239Last 12 Months9Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF