The Application of Emotion Valence Ratios in Facial Emotion Recognition for Detecting Depression Among Older Adults in Institutional Settings

Developing evidence-based parameters to enhance the reliability of face emotion recognition (FER) systems in detecting depression among the elderly is essential. This study aims to elucidate the relationship between the ratio of each emotion valence captured by the FER system and heart rate variability (HRV) while participants watch a video in relation to their depression scores. YOLO, an open-source data analysis toolkit, was used to extract three facial emotion valence features (neutral, positive, and negative) and determine the ratio of each emotion valence over time during video viewing. Additionally, HRV was assessed, and the Geriatric Depression Scale was administered to understand the correlation between FER parameters and depression scores.