[A study of cognitive impairment quantitative assessment method based on gait characteristics].

Autor: Tao S; Dalian Key Laboratory of Smart Medical and Health, Dalian University, Dalian, Liaoning 116622, P. R. China., Hu H; Dalian Key Laboratory of Smart Medical and Health, Dalian University, Dalian, Liaoning 116622, P. R. China., Kong L; Dalian Key Laboratory of Smart Medical and Health, Dalian University, Dalian, Liaoning 116622, P. R. China., Lyu Z; Internal Medicine-Neurology, Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center, Beijing 100176, P. R. China., Wang Z; Graduate School, Dalian University, Dalian, Liaoning 116622, P. R. China., Zhao J; Internal Medicine-Neurology, Zhongshan Hospital Affiliated to Dalian University, Dalian, Liaoning 116001, P. R. China., Liu S; Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin 300192, P. R. China.
Jazyk: čínština
Zdroj: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi [Sheng Wu Yi Xue Gong Cheng Xue Za Zhi] 2024 Apr 25; Vol. 41 (2), pp. 281-287.
DOI: 10.7507/1001-5515.202305019
Abstrakt: Alzheimer's disease (AD) is a common and serious form of elderly dementia, but early detection and treatment of mild cognitive impairment can help slow down the progression of dementia. Recent studies have shown that there is a relationship between overall cognitive function and motor function and gait abnormalities. We recruited 302 cases from the Rehabilitation Hospital Affiliated to National Rehabilitation Aids Research Center and included 193 of them according to the screening criteria, including 137 patients with MCI and 56 healthy controls (HC). The gait parameters of the participants were collected during performing single-task (free walking) and dual-task (counting backwards from 100) using a wearable device. By taking gait parameters such as gait cycle, kinematics parameters, time-space parameters as the focus of the study, using recursive feature elimination (RFE) to select important features, and taking the subject's MoCA score as the response variable, a machine learning model based on quantitative evaluation of cognitive level of gait features was established. The results showed that temporal and spatial parameters of toe-off and heel strike had important clinical significance as markers to evaluate cognitive level, indicating important clinical application value in preventing or delaying the occurrence of AD in the future.
Databáze: MEDLINE