Design of Interactive Artificial Intelligence for Early Cognitive Diagnosis

Abstract

Due to the aging population in South Korea, the proportion of elderly people aged 65 and over is expected to increase from 14.9% in 2019 to 46.5% by 2067. The number of elderly people per 100 working-age population (15-64 years old) is also anticipated to rise to 102.4 by 2067. Population aging is recognized as a social issue, leading to problems such as increased chronic diseases, higher levels of elderly isolation, and insufficient medical infrastructure. To solve the problem of cognitive decline, such as dementia due to the aging of the population, research is actively being conducted in various fields, such as simulating cognitive ability and learning, inference, prediction, and problem-solving using artificial intelligence deep learning technology in the form of a fusion of artificial intelligence and realistic content technology. This study is on an interactive cognitive early diagnosis training system using artificial intelligence. The speech recognition technology for early cognitive diagnosis uses Selvas AI (Artificial Intelligence)'s speech recognition STT (Speech to Text)-TTS (Text to Speech). AI speech recognition interaction can increase psychological safety through conversation with users. It evaluates cognition (dementia) using MMSE (Mini-Mental State Examination)-K (DS), and it is a system that evaluates seven cognitive areas through the CERAD (Consortium to Establish a Registry for Alzheimer Disease)-K analysis system. The design of interactive artificial intelligence for early cognitive diagnosis aims to improve the cognitive function and daily living abilities of the elderly population.