Intelligent Multimodal System for the Early Detection of Children's Needs and Emotions through Facial Expression Analysis

Abstract

At present, verbal communication between infants and their caregivers constitutes a clinical and everyday challenge, since many first-time parents and daycare staff often make ambiguous interpretations of the signals used by babies to express their needs. The objective of this research was to develop an intelligent system capable of detecting and classifying infants’ emotions in real time through the analysis of their gestures and facial expressions, as well as acoustic signals (crying), implemented and evaluated at the childcare center “El Nido.” The study was applied in nature and, according to its approach, quantitative, with a pre-experimental design. The population consisted of 100 infants; however, a non-probabilistic convenience sampling method was used, selecting a sample of 20 infants. Finally, the results showed that the Multimodal Intelligent System for the Early Detection of Infant Needs and Emotions through Real-Time Facial Expression Analysis achieved a 14% uncertainty rate and an 86% accuracy rate in detecting needs.