Alzheimer's Disease Detection Using Comprehensive Analysis of Timed Up and Go Test via Kinect V.2 Camera and Machine Learning

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 1589 - 1600
• Main Authors: Seifallahi, Mahmoud, Mehraban, Afsoon Hasani, Galvin, James E., Ghoraani, Behnaz
• Format: Journal Article
• Language: English
• Published: United States IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aged; Aging; Alzheimer Disease - diagnosis; Alzheimer's disease; Alzheimer’s disease (AD); Assessments; Balance; Cameras; Cognitive ability; Depression; Disease detection; Diseases; Feasibility studies; Feature extraction; Gait; Humans; Kinect V.2 camera; Learning algorithms; Legged locomotion; Low cost; Machine Learning; Neurodegenerative Diseases; Older people; Patients; Postural Balance; Quantitative analysis; Recording; Signal processing; Signs and symptoms; skeletal data; Statistical analysis; support vector machine (SVM); Support vector machines; Time and Motion Studies; timed up and go (TUG)
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2022.3181252

Alzheimer's disease (AD) is a progressive neurodegenerative disease affecting cognitive and functional abilities. However, many patients presume lower cognitive or functional abilities because of aging and do not undergo clinical assessments until the symptoms become too advanced. Developing a low-cost and easy-to-use AD detection tool, which can be used in any clinical or non-clinical setting, can enable widespread AD assessments and diagnosis. This paper investigated the feasibility of developing such a tool to detect AD vs. healthy control (HC) from a simple balance and walking assessment called the Timed Up and Go (TUG) test. We collected joint position data of 47 HC and 38 AD subjects as they performed TUG in front of a Kinect V.2 camera. Our signal processing and statistical analyses provided a comprehensive analysis of balance and gait with 12 significant features for discriminating AD from HC after adjusting for age and the Geriatric Depression Scale. Using these features and a support vector machine classifier, our model classified the two groups with an average accuracy of 97.75% and an F-score of 97.67% for five-fold cross-validation and 98.68% and 98.67% for leave-one-subject out cross-validation. These results demonstrate the potential of our approach as a new quantitative complementary tool for detecting AD among older adults. Our work is novel as it presents the first application of Kinect V.2 camera and machine learning to provide a comprehensive and quantitative analysis of the TUG test to detect AD patients from HC. This study supports the feasibility of developing a low-cost and convenient AD assessment tool that can be used during routine checkups or even at home; however, future investigations could confirm its clinical diagnostic value in a larger cohort.