Enhancing elderly care services through integrated sentiment analysis and knowledge reasoning: A deep learning approach

Abstract

This study proposes a pioneering integrated care model for elderly care service robots that integrates sentiment analysis and knowledge reasoning through a deep learning framework. The primary objective of this research is to address the limitations of current elderly care robots in providing emotionally intelligent and personalized care. The model utilizes advanced deep learning techniques, such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), to analyze multimodal data comprising speech, facial expressions and body language. This enables the model to provide a comprehensive understanding of an elderly individual's emotional and health status. The efficacy of the model is demonstrated by its ability to enhance the precision of care decisions, improve the quality of care, user satisfaction, and system reliability. The experimental results demonstrate substantial improvements in sentiment recognition accuracy (96.5 %), reasoning accuracy (93.7 %), decision execution time (3.2 s), user satisfaction (4.9 points), and system stability (98.4 %), highlighting the transformative potential of the model in revolutionizing elderly care services.