A Deep Learning–Driven AI System for Real-Time Emotional Support to Visually Impaired Users for Inclusive Well-Being

The visually impaired community is often affected emotionally and psychologically by limitations in non-verbal communication, reduced social interaction, and diminished ability to perceive environmental movements. These challenges highlight the need for inclusive technologies that enhance emotional stability, mental well-being, and social equity, aligning with global initiatives focused on Good Health and Well-Being and Reduced Inequalities. This paper presents a smart deep learning-based AI system that provides real-time emotional support to visually impaired users by analyzing speech signals, acoustic context, and environmental audio through multimodal analysis. The system generates tailored emotional support using a context-driven reasoning engine and a dynamic response generation module. The proposed approach is novel in its integration of multimodal emotion inference with an accessibility-centered conversational feedback loop, which is not achievable with existing assistive technologies. Experimental evaluations demonstrate high emotion recognition accuracy, low processing latency, and strong user engagement. The results indicate that the proposed system has significant potential to contribute to inclusive, human-centered, and sustainable AI solutions that enhance emotional comfort and everyday well-being for visually impaired individuals.