Context-Based Emotion Recognition and ASD Intervention: A Deep Learning Approach with Restricted Boltzmann Machines

Abstract

Introduction: Individuals with Autism Spectrum Disorder (ASD) struggle with social communication and emotional recognition. Traditional interventions have limitations, but emerging emotion recognition technologies offer potential improvements. This study explores the use of context-aware emotion recognition to enhance ASD interventions.

Objectives: The main objectives of this research is to integrate Context-Based emotion recognition in ASD interventions. Develop and assess methods to improve social skills and emotional awareness. Analyze the impact of contextual factors on recognition accuracy. Compare RBM and DBN models for emotion classification. Support multimodal, context-based ASD intervention strategies.

Methods: The proposed methodology utilizes the EMOTIC dataset to train emotion recognition models using deep learning techniques, specifically RBM and DBNs. The research employs an ablation study to analyze the impact of contextual factors such as multi-modalities, situational/background context, and inter-agent interactions. Model performance is evaluated using metrics such as precision, recall, F1 score, specificity, K-S statistic, and Gini coefficient. Additionally, a comparative analysis of emotion recognition methods is performed, with a focus on the mean Average Precision (mAP) scores achieved by RBM.

Results: The results indicate strong recognition accuracy for specific emotional states while identifying areas for improvement. The inclusion of multiple modalities and contextual factors such as Affection, Engagement, Anger, and Excitement significantly enhances emotion recognition accuracy. The ablation study confirms that integrating situational and background context improves classification performance. The comparative analysis highlights RBM’s superior mAP scores compared to other techniques. These findings suggest that a context-aware, multimodal approach is beneficial for improving emotion recognition in ASD individuals.

Conclusions: Context-aware, multimodal emotion recognition improves ASD intervention effectiveness. The findings highlight the need for personalized, data-driven approaches to enhance social communication and emotional intelligence in ASD individuals.