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

TL;DR

This paper develops and evaluates an emotion recognition module for a virtual coach aimed at engaging older adults, demonstrating the effectiveness of multimodal approaches across different cultures.

Contribution

It presents a tailored multimodal emotion recognition system for older adults interacting with a virtual coach, including data collection, annotation, and performance analysis across modalities and cultures.

Findings

Multimodal methods outperform unimodal ones in emotion recognition accuracy.

Audio-based recognition achieved around 68% accuracy.

Video-based recognition achieved 72-74% accuracy.

Abstract

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.