Detection of Mild Cognitive Impairment from Quantitative Analysis of Timed Up and Go (TUG)

Abstract

Mild cognitive impairment (MCl) is abnormal cognitive decline beyond expected normal decline. The rate of progression to Alzheimer's disease (AD) in people with MCl is an estimated 80% in 6 years. However, identifying MCI from normal cognition in older adults remains a clinical challenge in early AD detection. We investigated a new method for detecting MCI based on patients' gait and balance. Our approach performs a comprehensive analysis of the Timed Up and Go test (TUG), based on the first application of a Kinect v.2 camera to record and provide movement measures and machine learning to differentiate between the two groups of older adults with MCI and healthy controls (HC). We collected movement data from 25 joints of the body via a Kinect v.2 camera as 30 HC and 25 MCI subjects performed TUG. The collected data provided a comprehensive list of gait and balance measures with 61 features, including duration of TUG, duration and velocity of transition phases, and micro and macro gait features. Our analysis evidenced that 25 features were significantly different between MCI and HC subjects, where 20 of them were unique features as indicated by our correlation analysis. The classification results using three different classifiers of support vector machine (SVM), random forest, and artificial neural network showed that the ability of our approach for detecting MCI subjects with the highest performance was using SVM with 94% accuracy, 100 % precision, 93.33% F-score, and 0.94 AUC. These observations suggest the possibility of our approach as a low-cost, easy-to-use MCI screening tool for objectively detecting subjects at high risk of developing AD. Such a tool is well-suited for widespread application in clinical settings and nursing homes to detect early signs of cognitive impairment and promote healthy aging.