The EMPATHIC project aimed to design an emotionally expressive virtual coach capable of engaging healthy seniors to improve well-being and promote independent aging. In particular, the system's human sensing capabilities allow for the perception of emotional states to provide a personalized experience. This paper outlines the development of the emotion expression recognition module of the virtual coach, encompassing data collection, annotation design, and a first methodological approach, all tailored to the project requirements. With the latter, we investigate the role of various modalities, individually and combined, for discrete emotion expression recognition in this context: speech from audio, and facial expressions, gaze, and head dynamics from video. The collected corpus includes users from Spain, France, and Norway, and was annotated separately for the audio and video channels with distinct emotional labels, allowing for a performance comparison across cultures and label types. Results confirm the informative power of the modalities studied for the emotional categories considered, with multimodal methods generally outperforming others (around 68% accuracy with audio labels and 72-74% with video labels). The findings are expected to contribute to the limited literature on emotion recognition applied to older adults in conversational human-machine interaction, and guide the development of future systems.

This paper investigates effects of participants’ gender and age (adolescents, young adults, and seniors), robots’ gender (male and female robots) and appearance (humanoid vs android) on robots’ acceptance dimensions. The study involved 6 differently aged groups of participants (two adolescents, two young adults and two seniors’ groups, for a total of 240 participants) requested to express their willingness to interact and their perception of robots’ usefulness, pleasantness, appeal, and engagement for two different sets of females (Pepper, Erica, and Sophia) and male (Romeo, Albert, and Yuri) humanoid and android robots. Participants were also requested to express their preferred and attributed age ranges and occupations they entrusted to robots among healthcare, housework, protection and security and front office. Results show that neither the age nor participants and robots’ gender, nor robots’ human likeness univocally affected robots’ acceptance by these differently aged users. Robots’ acceptance appeared to be a nonlinear combination of all these factors.

This study investigates how the ability to recognize static facial emotional expressions changes over time, specifically through three developmental stages: childhood, preadolescence, and adolescence. A total of 301 Italian participants were involved and divided into three age groups: children (7–10 years), pre-adolescents (11–13 years), and adolescents (14–19 years). Participants completed an online emotional decoding task using images from the Child Affective Facial Expression (CAFE) database, depicting anger, disgust, fear, happiness, sadness, surprise, and neutrality, conveyed by children of different ethnicities (African American, Caucasian/European American, Latino, and Asian). Results indicated that female participants generally exhibited a higher emotion recognition accuracy than male participants. Among the emotions, happiness, surprise, and anger were the most accurately recognized, while fear was the least recognized. Adolescents demonstrated a better recognition of disgust compared to children, while pre-adolescents more poorly recognized neutrality compared to children and adolescents. Additionally, this study found that female facial expressions of disgust, sadness, and fear were more accurately recognized than male expressions, whereas male expressions of surprise and neutrality were better recognized than female expressions. Regarding the ethnicity of facial expressions, results revealed that ethnicity can be better or more poorly recognized depending on the emotion investigated, therefore presenting very heterogeneous models.

Considered the increasing use of assistive technologies in the shape of virtual agents, it is necessary to investigate those factors which characterize and affect the interaction between the user and the agent, among these emerges the way in which people interpret and decode synthetic emotions, i.e., emotional expressions conveyed by virtual agents. For these reasons, an article is proposed, which involved 278 participants split in differently aged groups (young, middle-aged, and elders). Within each age group, some participants were administered a “naturalistic decoding task,” a recognition task of human emotional faces, while others were administered a “synthetic decoding task” namely emotional expressions conveyed by virtual agents. Participants were required to label pictures of female and male humans or virtual agents of different ages (young, middle-aged, and old) displaying static expressions of disgust, anger, sadness, fear, happiness, surprise, and neutrality. Results showed that young participants showed better recognition performances (compared to older groups) of anger, sadness, and neutrality, while female participants showed better recognition performances (compared to males) of sadness, fear, and neutrality; sadness and fear were better recognized when conveyed by real human faces, while happiness, surprise, and neutrality were better recognized when represented by virtual agents. Young faces were better decoded when expressing anger and surprise, middle-aged faces were better decoded when expressing sadness, fear, and happiness, while old faces were better decoded in the case of disgust; on average, female faces where better decoded compared to male ones.