Artificial Intelligence in Gait Analysis for the Prediction and Verification of Cognitive Change in Patients with Mild Cognitive Impairment

• Main Author: 連玠雯
• Other Authors: SHAW, JIN-SIANG
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/f72655

碩士 === 國立臺北科技大學 === 機械工程系機電整合碩士班 === 107 === Mild cognitive impairment (MCI) refers to a transitional condition between normal aging and early dementia, but not all MCI become dementia. Dementia is a common neurodegenerative disorder. Degradation of the cognitive function of MCI affects the performance of walking. In this study, a neuropsychological test was executed and there were two stages of gait test. First, a portable gait analysis system was used to obtain gait features and did the neuropsychological test. After half a year, tested gait again. Cross-section and longitudinal research were performed in this paper. The cross-sectional research used the first stage gait result to establish a classification model for different types of MCI. The predictive results were 91.67% accurate for PD-MCI and non-PD-MCI patients, confirming the clinical diagnosis of MCI patients. Besides, the longitudinal research built a classification model for the difference between the first and second stage results that can be used to predict future gait declines. The result shown the most critical gait parameter for MCI patients becoming dementia may be the speed of walking. It is expected to assist the doctors in diagnosing MCI patients, making the speed in gait a biomarker for diagnosing MCI.