AI-based emotion recognition in dementia through facial expression: A scoping review

Abstract

Emotion assessment in dementia care is vital for patient well-being and effective care planning. Traditional methods are often subjective and time-consuming. This study examines the use of AI-based facial expression analysis for emotion recognition in dementia patients. A scoping review was conducted using the SPIDER strategy. Five databases—PubMed, Scopus, PsycInfo, ProQuest, and IEEE Xplore —were consulted, with additional records identified through snowballing. Data on participant characteristics, intervention details, non-AI comparisons, and clinical outcomes were categorized. Two authors independently screened records and extracted data on AI driven tools. The review analyzed 11 studies, primarily using deep neural networks. While most studies relied on pre-existing datasets, some collected original data. The studies focused on assessing a variety of emotions, with an emphasis on detecting basic emotions and, in some cases, more complex emotional states. AI applications included early detection, diagnosis, intervention impact assessment, and reliability testing. Comparisons were made with traditional assessment tools. This scoping review highlights the potential of AI tools to improve dementia care. However, standardized data collection and processing protocols are needed to advance AI in emotion recognition for dementia patients. Integrating multiple data sources and addressing dataset limitations are crucial for improving model accuracy and representativeness. Ethical considerations, including privacy and data security, must be prioritized when developing and implementing AI tools in this population. Interdisciplinary collaboration is essential to fully harness their potential.