Empathetic AI: Multimodal Interaction Framework for Emotional Resilience and Cognitive Engagement in Elderly Care

Harnessing the power of artificial intelligence, Large Language Models (LLMs) such as OpenAI’s GPT-4 and Google’s Gemini revolutionize eldercare by addressing emotional, cognitive, and social well-being. These systems transcend conventional mental health support by serving as empathetic virtual companions, dynamically adapting to users' emotional states through multimodal interaction, including conversational chatbots, voice assistants, and lifelike deepfake avatars. This system contains memory recall modules that allow patients to resemble significant life moments, offer personalized cognitive exercises, and make use of amiable motivational strategies to help them become mental gymnastics experts. The technology is intended for both the advanced and the less tech-savvy ones, so it is equipped with multilingual functionalities, user-friendly interfaces, big buttons, and voice input functions. Real-time emotional adaptation is powered by advanced AI sentiment analysis, ensuring tailored and empathetic responses. The fundamental change of the care of the elderly ladies and men leads to the fact that an innovative system was established, which is empathetic, user-friendly, and available. New technology supports the aging population in a personalized manner that respects the particular needs such as the ones mentioned above.

Authors

Institutions

Publication Details

Published
2025-03-28
DOI
https://doi.org/10.1109/ngise64126.2025.11085267
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Empathetic AI: Multimodal Interaction Framework for Emotional Resilience and Cognitive Engagement in Elderly Care

A. Anny Leema, Arun Kumar Sangaiah, P. Balakrishnan
1 citations
AI in Service Interactions
2.48
article

Empathetic AI: Multimodal Interaction Framework for Emotional Resilience and Cognitive Engagement in Elderly Care

A. Anny Leema, Arun Kumar Sangaiah, P. Balakrishnan
article en
1 citations

Abstract

Harnessing the power of artificial intelligence, Large Language Models (LLMs) such as OpenAI’s GPT-4 and Google’s Gemini revolutionize eldercare by addressing emotional, cognitive, and social well-being. These systems transcend conventional mental health support by serving as empathetic virtual companions, dynamically adapting to users' emotional states through multimodal interaction, including conversational chatbots, voice assistants, and lifelike deepfake avatars. This system contains memory recall modules that allow patients to resemble significant life moments, offer personalized cognitive exercises, and make use of amiable motivational strategies to help them become mental gymnastics experts. The technology is intended for both the advanced and the less tech-savvy ones, so it is equipped with multilingual functionalities, user-friendly interfaces, big buttons, and voice input functions. Real-time emotional adaptation is powered by advanced AI sentiment analysis, ensuring tailored and empathetic responses. The fundamental change of the care of the elderly ladies and men leads to the fact that an innovative system was established, which is empathetic, user-friendly, and available. New technology supports the aging population in a personalized manner that respects the particular needs such as the ones mentioned above.

National Yunlin University of Science and Technology (TW), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 9%
AI in Service Interactions
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.