A detection model of cognitive impairment via the integrated gait and eye movement analysis from a large Chinese community cohort.

ABSTRACT:

Introduction

Whether the integration of eye-tracking, gait, and corresponding dual-task analysis can distinguish cognitive impairment (CI) patients from controls remains unclear.

Methods

One thousand four hundred eighty-one participants, including 724 CI and 757 controls, were enrolled in this study. Eye movement and gait, combined with dual-task patterns, were measured. The LightGBM machine learning models were constructed.

Results

A total of 105 gait and eye-tracking features were extracted. Forty-six parameters, including 32 gait and 14 eye-tracking features, showed significant differences between two groups (P < 0.05). Of these, the Gait_3Back-TurnTime and Dual-task cost-TurnTime patterns were significantly correlated with plasma phosphorylated tau 181 (p-tau181)

SUBMITTER: Lin J 

PROVIDER: S-EPMC10916936 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature