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

Abstract

— Alzheimer’sdisease (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-validationand 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.

Certain Studies on Alzheimer's disease: A Comprehensive Review

“…Additionally, the technique showed that it was possible to differentiate between the three stages of the illness. The study investigated the feasibility of creating a tool to differentiate between AD and HC persons utilizing information from the Timed Up and Go (TUG) test, a straightforward assessment of walking and balance [29]. When conducting the TUG test in front of a Kinect V.2 camera, joint position data from 47 HC and 38 AD individuals was gathered.…”

Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy

“…10-fold cross-validation means that when training the model, the trained samples are divided into 10 parts, where 1 part of the data is left to validate the model and the remaining 9 samples are trained. The cross validation is repeated 10 times and the average of the 10 test results is used as the final result [49].…”

A wearable telehealth system for the monitoring of parameters related to heart failure

“…Both solutions are not only expensive but also have surgical risks [ 5 ]. Most of the commercially available devices are not in accordance with the World Health Organization's (WHO) ASSURED criteria for Affordable, Sensitive, Specific, User-friendly, Rapid and robust, Equipment-free and Deliverable devices [ [12] , [13] , [14] ]. Therefore, there is a need for a non-invasive solution that can provide continuous and real-time monitoring of parameters related to HF.…”