The detection of age groups by dynamic gait outcomes using machine learning approaches.

ABSTRACT: Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the present study was to classify healthy young-middle aged, older adults and geriatric patients based on dynamic gait outcomes. Classification performance of three supervised machine learning methods was compared. From trunk 3D-accelerations of 239 subjects obtained during walking, 23 dynamic gait outcomes were calculated. Kernel Principal Component Analysis (KPCA) was applied for dimensionality reduction of the data for Support Vector Machine (SVM) classification. Random Forest (RF) and Artificial

SUBMITTER: Zhou Y 

PROVIDER: S-EPMC7064519 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature