A preliminary analysis of relationship between dual-task gait and age-related cognitive ability using neural network models

Abstract

BackgroundAging causes declines in cognitive and motor functions, often manifested in altered gait. Dual-task gait is a sensitive marker for early functional changes. This study investigated whether neural network (NN) models can differentiate gait patterns across age groups under dual-task conditions, and which joint features contribute most to age classification.MethodsThirty-six healthy were recruited for three groups: young (18-37 years), middle-aged (38-57), and older adults (≥58), with 12 in each. Gait data were collected under dual-task conditions (reverse-counting while walking) using a Vicon motion capture system and force plates. The outcomes from gait analysis included spatiotemporal parameters, full-body joint angles, and lower limb joint force, moment and power. Eleven kinds of NN models with different combinations of gait outcomes were constructed using NNs to classify age groups.ResultsThe NN model combining all kinematics, kinetics, and spatiotemporal parameters achieved the highest classification accuracy (95.70 ± 3.15%), with F1 scores (the harmonic mean of precision and recall) above 0.948 across all age groups. Among single-feature models, the full-body kinematic model performed best (94.17 ± 3.47%), while the spatiotemporal model showed the lowest accuracy. The middle-aged group achieved the highest accuracy in the full-body kinematic model. Ankle-related features were a main factor in contributing to the NN models.ConclusionNN models with all-parameters can accurately classify age-related dual-task gait differences. By capturing gait changes, NN offers a quantitative tool for early detection of motor and cognitive decline, with potential applications in fall risk prediction, dementia screening, and targeted rehabilitation.