Emotion recognition and expression are essential components of human-agent interaction, particularly for users with limited verbal communication abilities. This pilot study explores the potential of personalized color-based emotion visualization to support emotional understanding and interpersonal communication among students with developmental and communication-related disorders. Eight students from a special needs school participated in a dyadic task, watching short animation clips and explaining them to a peer under two conditions: with and without projected color cues. Color-emotion mappings were co-constructed through a preliminary survey based on each pair’s associations. While the small sample size limited statistical significance, trends suggested that color visualization may enhance emotional valence, interpersonal physiological synchronization, and affective interpretation. These findings offer preliminary insights into how adaptive, user-defined visual cues can inform the design of emotionally supportive agents. To address the limitations of this study, future work will recruit a larger cohort across specific diagnostic groups and evaluate the integration of these mechanisms into embodied agents, examining their effects on user engagement and emotional alignment in real-world interactions.

The video describes the novel concept of “culturally competent robotics”, which is the main focus of the project CARESSES (Culturally-Aware Robots and Environmental Sensor Systems for Elderly Support). CARESSES a multidisciplinary project whose goal is to design the first socially assistive robots that can adapt to the culture of the older people they are taking care of. Socially assistive robots are required to help the users in many ways including reminding them to take their medication, encouraging them to keep active, helping them keep in touch with family and friends. The video describes a new generation of robots that will perform their actions with attention to the older person's customs, cultural practices and individual preferences.

This work proposes a system able to recognize the user habits based on his daily activities in a smart house. The habit estimation system uses the information provided by an activity recognition module, which provides the sequence and the duration of the activities performed by the user. Based on those parameters, the activities are represented as a signal by using Fourier series representation. Several output signals from different users are clustered into groups using the k-means method, where each cluster corresponds to a specific habit from a group of people. The proposed system was tested with dataset from the experiment that took place in an environment similar to a smart house. The users were asked to perform a set of 6 activities in any desired orders. In total, twenty-four subjects took part in the experiments. All activities were successfully recognized by the system and three different habits were found. The proposed system along with its habit representation can be potentially used to trace the relationships between the habits observed and some aspects of the user personality traits.

Many children on autism spectrum have atypical behavioral expressions of engagement compared to their neu-rotypical peers. In this paper, we investigate the performance of deep learning models in the task of automated engagement estimation from face images of children with autism. Specifically, we use the video data of 30 children with different cultural backgrounds (Asia vs. Europe) recorded during a single session of a robot-assisted autism therapy. We perform a thorough evaluation of the proposed deep architectures for the target task, including within- and across-culture evaluations, as well as when using the child-independent and child-dependent settings. We also introduce a novel deep learning model, named CultureNet, which efficiently leverages the multi-cultural data when performing the adaptation of the proposed deep architecture to the target culture and child. We show that due to the highly heterogeneous nature of the image data of children with autism, the child-independent models lead to overall poor estimation of target engagement levels. On the other hand, when a small amount of data of target children is used to enhance the model learning, the estimation performance on the held-out data from those children increases significantly. This is the first time that the effects of individual and cultural differences in children with autism have empirically been studied in the context of deep learning performed directly from face images.

Cultural competence is a well known requirement for an effective healthcare, widely investigated in the nursing literature. We claim that personal assistive robots should likewise be culturally competent, aware of general cultural characteristics and of the different forms they take in different individuals, and sensitive to cultural differences while perceiving, reasoning, and acting. Drawing inspiration from existing guidelines for culturally competent healthcare and the state-of-the-art in culturally competent robotics, we identify the key robot capabilities which enable culturally competent behaviours and discuss methodologies for their development and evaluation.

Socially assistive robots have been used successfully for improvement of communication skills of young children. Such robots need to attract children's attention and stimulate sustainable interactions. Many recent research studies propose various approaches for increasing of interaction effectiveness of such robots. In this study, we investigate the effect of robotic movements with animacy on the visual attention of young children. For the experiments, we used the TurtleBot robot. The effect of three different robot movement patterns were explored with the conducted tests: simple circle movement, inanimated movement, and animated movement. Thirty four children (21 males, 13 females, Mean of ages: 4.5) participated in an experiment. Results show that robot movement raises visual attention. Most of the children demonstrated increased interest toward animated robotic movement than simple motion. The animated movement that elicits much interest depends on children's age. The findings of this study contribute to effective use of interactive robots in education and therapy of young children and children with developmental disorder.

In this article, we present two robot design types: human-like and product-like robot. A human-like robot is designed to be similar to humans in the appearance or behavior. A product-like robot is designed to maximize the functionality of a product. As well as the comparison between the human-like robot and product-like robot, cultural differences between Korea and Japan on perceived intellectual capability, robot-likeness, usefulness, overall impression and purchase intention were investigated. People evaluated the intellectual capability, usefulness and overall impression of the robots differently depending on their nationalities. The human-like robot was considered to be more robot-like than the product-like robot whereas there was no difference between the robots in both cultures on purchase intention. Implications for the robot design between Korea and Japan are discussed.

It is an advantage to use robots in autism therapy since they provide repetitive stimuli during the learning of social skills. A detailed exploration is required to design assistive robots for autism therapy with higher effectiveness. This study has investigated the effectiveness of an interactive robot's feedback during autism therapy for improving specific social skills of the children. The experiment described in this study is that children with autism engaged with an interactive system to perform affective touch behaviour and received the robot's visual and auditory feedback. Results showed that the presence of an interactive robot's feedback triggered more effective children's touch behaviour.

Previous researches, in the field of robot assisted therapy, suggested that communications with robots encourage autistic children to have more positive reaction to the robots' behavior, and this seems to be a reliable approach to improve the social interaction and communication skills of autistic children. However, most of the researches focused on improving only some limited aspects of the autistic children's social communication problems and it is difficult to evaluate the training with current technology. Thus, in the present study, we suggest a training robot to improve the adjustability of autistic children's way of communications through touching. First, we explore three different ways of touching and compare different force levels of touching for autistic children to those for non-autistic children. As a result, non-autistic children have shown a definite pattern on the way of touching, while there is no pattern in autistic children's one. Second, we investigate the efficiency of therapy with the robot, when the robot shows its expression to a person with autism. As a result, the participant has indicated that better success rate with the presence of expression from robot.