  The EMPATHIC project aimed to design an emotionally expressive virtual coach
capable of engaging healthy seniors to improve well-being and promote
independent aging. One of the core aspects of the system is its human sensing
capabilities, allowing 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.
