The workshop aims to bring together new interdisciplinary approaches to socio-emotional learning, exploring how interactive artifacts offer the possibility of translating emotions into tangible and physical experiences. By engaging participants in project-based prototyping activities, the workshop will investigate how tangible artifacts can foster interdisciplinary discussion, as well as define concrete principles to transform personal emotions into accessible and shared experiences.

Assessing the usability of interactive systems for preschool children poses considerable difficulties due to their restricted verbal abilities, short attention spans, and tendency to favor adult researchers. Moreover, although numerous technologies for children are intended for group interaction, typical assessment approaches are still predominantly focused on individual use. This work presents the Ape-raisal, a tangible, collaborative usability assessment tool inspired by a "monkey and bananas" analogy. The tool converts a 5-point Likert scale into a tangible, fabric-based tool where children work together to "feed" monkeys to indicate their levels of engagement and satisfaction. We carried out a pilot study involving 32 children divided into six groups, utilizing a parallel triangulation method that integrated children’s self-reported information from the Ape-raisal with a systematic researcher observation framework. Our results indicate a connection between the concrete reports (Mean = 4.06, Median = 5) and the visible behavioral signs of high engagement and usability. These findings indicate that tangible and cooperative tools can successfully enable young children to participate as co-designers, offering trustworthy feedback in a group-oriented setting.

This poster presents an art-inspired iterative design approach applied to the definition of two experimental scenarios for movement data collection. Scenarios are inspired by warm-up exercises dancers perform to broaden group consciousness. Research focuses on the influence an expert dancer exerts on novice dancers by propagating her own movement patterns (individual motor signatures), for stimulating the emergence of a group movement pattern (group motor signature).

There is an increasing interest in combining interactive technology with food, leading to a new research area called human-food interaction. While food experiences are increasingly benefiting from interactive technology, for example in the form of food tracking apps, 3D-printed food and projections on dining tables, a more systematic advancement of the field is hindered because, so far, there is no comprehensive articulation of the grand challenges the field is facing. To further and consolidate conversations around this topic, we invited 21 HFI experts to a 5-day seminar. The goal was to review our own and prior work to identify the grand challenges in human-food interaction. The result is an articulation of 10 grand challenges in human-food interaction across 4 categories (technology, users, design and ethics). By presenting these grand challenges, we aim to help researchers move the human-food interaction research field forward.

Research focusing on social interaction often leverages data-sets, allowing annotation, analysis, and modeling of social behavior. When it comes to commensality, researchers have started working on computational models of food and eating-related activities recognition. The growing research area known as Digital Commensality, has focused on meals shared online, for instance, through videochat. However, to investigate this topic, traditional data-sets recorded in laboratory settings may not be the best option in terms of ecological validity. Covid-19 restrictions and lock-downs have increased in online gatherings, with many people becoming used to the idea of sharing meals online. Following this trend, we propose the concept of collecting data by recording online interactions and discuss the challenges related to this methodology. We illustrate our approach in creating the first Digital Commensality data-set, containing recordings of food-related social interactions collected online during the Covid-19 outbreak.

Despite the large number of computational resources for emotion recognition, there is a lack of data sets relying on appraisal models. According to Appraisal theories, emotions are the outcome of a multi-dimensional evaluation of events. In this paper, we present APPReddit, the first corpus of non-experimental data annotated according to this theory. After describing its development, we compare our resource with enISEAR, a corpus of events created in an experimental setting and annotated for appraisal. Results show that the two corpora can be mapped notwithstanding different typologies of data and annotations schemes. A SVM model trained on APPReddit predicts four appraisal dimensions without significant loss. Merging both corpora in a single training set increases the prediction of 3 out of 4 dimensions. Such findings pave the way to a better performing classification model for appraisal prediction.

With one-person households being increasingly common and Covid-19 lockdown policies forcing people to stay home, remote dining has become common practice for many, who take it as an opportunity to connect with others in times of loneliness. Sharing meals online, also known as digital commensality, is a rich form of interaction, where people leverage technology to achieve a sense of connectedness and belonging while eating. In this paper, we look at digital commensality and we explore its inherent playful potential with the aim to inspire the design of engaging technologies that can support, enhance and augment this form of interaction. For this, we used a situated play design approach to document and analyze the behavior of 36 people (including pairs of friends and strangers) sharing meals online. Our analysis surfaced a set of play potentials of remote dining -- i.e., playful things people already do and enjoy spontaneously while sharing meals online. We present those play potentials as inspirational material: they can motivate and enrich the design of future digital commensality technologies by responding to people's desire for playful and social interaction with, through, and around food.

This Workshop aims at stimulating multi-disciplinary discussions about the challenges related to corpus creation and annotation for social skills behavior analysis. Contributions from computational, psychological and psychometrics perspectives, as well as applications including platforms to share corpora and annotations, are welcomed. The main challenges related to corpus creation include the choice of the best setup and sensors, finding a trade-off between eliciting natural interactions, limiting invasiveness and collecting precise information. The second issue in this context regards the process of annotation. The choice of the type of annotators (experts vs. nonexperts), the type of annotations (automatic vs. manual, continue vs. discrete), the temporal segmentation (windowed vs. holistic) is crucial for a correct measure of the phenomenon of interest and getting significant results. The topics of CATS2021 will have a strong impact on researchers and stakeholders across different disciplines, such as Computer Science, Social Signal Processing, Psychology, Statistics. Leveraging the opportunities offered by such a multidisciplinary environment, the participants could enrich their perspective, strengthen their practices and methodologies and draw together a research roadmap tackling the discussed challenges, which might be taken up in future collaborations.

In this paper, we present and assess a novel technique for unitizing inspired by a cognitive theory on event structure perception. Unitizing indicates the process of dividing an observation into smaller units. Unitizing is often performed automatically, e.g., by selecting fixed-length windows. Although fast, such approach might result in unit boundaries being placed mid-interaction, eventually affecting observation, annotation, and labeling. We conceived a unitizing technique based on the Event Segmentation theory. In brief, changes drive the perception of boundaries between events (or units): an unexpected change in the observed situation might mean the current event ended and a new one begun. Our technique relies on observed changes for identifying unit boundaries. The first sketch of our technique was recently tested, proving it effective in overcoming the aforementioned shortcomings of fixed-window unitizing. Here, we further explore its feasibility by testing it in a different domain, i.e., solo stage performances, in order to explore the feasibility of adopting our unitizing approach across domains. Our results further support the idea of leveraging the Event Segmentation Theory for the design of an automatic technique for video unitizing.

Multimodal interfaces pose the challenge of dealing with the multi-ple interactive time-scales characterizing human behavior. To dothis, innovative models and time-adaptive technologies are needed,operating at multiple time-scales and adopting a multi-layered ap-proach. The first International Workshop on Multi-Scale MovementTechnologies, hosted virtually during the 22nd ACM InternationalConference on Multimodal Interaction, is aimed at providing re-searchers from different areas with the opportunity to discuss thistopic. This paper summarizes the activities of the workshop andthe accepted papers

This paper investigates how unitizing affects external observers' annotation of group cohesion. We compared unitizing techniques belonging to these categories: interval coding, continuous coding, and a technique inspired by a cognitive theory on event perception. We applied such techniques for sampling coding units from a set of recordings of social interactions rich in behaviors related to cohesion. Then, we compared the cohesion scores the observers assigned to each coding unit. Results show that the three techniques can lead to suitable ratings and that the technique inspired to cognitive theories leads to scores reflecting variability in cohesion better than the other ones.

The rising popularity of learning techniques in data analysis has recently led to an increased need of large-scale datasets. In this study, we propose a system consisting of a VR game and a software platform designed to collect the player’s multimodal data, synchronized with the VR content, with the aim of creating a dataset for emotion detection and recognition. The game was implemented ad-hoc in order to elicit joy and frustration, following the emotion elicitation process described by Roseman’s appraisal theory. In this preliminary study, 5 participants played our VR game along with pre-existing ones and self-reported experienced emotions.