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The EMPATHIC project aimed to design an emotionally expressive virtual coachcapable of engaging healthy seniors to improve well-being and promoteindependent aging. One of the core aspects of the system is its human sensingcapabilities, allowing for the perception of emotional states to provide apersonalized experience. This paper outlines the development of the emotionexpression recognition module of the virtual coach, encompassing datacollection, annotation design, and a first methodological approach, alltailored to the project requirements. With the latter, we investigate the roleof various modalities, individually and combined, for discrete emotionexpression recognition in this context: speech from audio, and facialexpressions, gaze, and head dynamics from video. The collected corpus includesusers from Spain, France, and Norway, and was annotated separately for theaudio and video channels with distinct emotional labels, allowing for aperformance comparison across cultures and label types. Results confirm theinformative power of the modalities studied for the emotional categoriesconsidered, with multimodal methods generally outperforming others (around 68%accuracy with audio labels and 72-74% with video labels). The findings areexpected to contribute to the limited literature on emotion recognition appliedto older adults in conversational human-machine interaction.

Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach