Exploring Emotion Expression Recognition in Older Adults Interacting with a Virtual Coach
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionSimilar Papers 제목 키워드 기반
Emotion Detection in Older Adults Using Physiological Signals from Wearable Sensors
Emotion detection in older adults is crucial for understanding their cognitive and emotional well-being, especially in hospital and assisted living environments. In this work, we investigate an edge-based, non-obtrusive …
Emotion RecognitionExperimenting with Affective Computing Models in Video Interviews with Spanish-speaking Older Adults
Understanding emotional signals in older adults is crucial for designing virtual assistants that support their well-being. However, existing affective computing models often face significant limitations: (1) limited avai…
Facial Expression RecognitionSentiment AnalysisSER_AMPEL: a multi-source dataset for speech emotion recognition of Italian older adults
In this paper, SER_AMPEL, a multi-source dataset for speech emotion recognition (SER) is presented. The peculiarity of the dataset is that it is collected with the aim of providing a reference for speech emotion recognit…
Emotion RecognitionSpeech Emotion RecognitionMECO: A Multimodal Dataset for Emotion and Cognitive Understanding in Older Adults
While affective computing has advanced considerably, multimodal emotion prediction in aging populations remains underexplored, largely due to the scarcity of dedicated datasets. Existing multimodal benchmarks predominant…
Emotion RecognitionHow Age Influences the Interpretation of Emotional Body Language in Humanoid Robots -- long paper version
This paper presents an empirical study investigating how individuals across different age groups, children, young and older adults, interpret emotional body language expressed by the humanoid robot NAO. The aim is to off…