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

TY - JOUR

T1 - A preliminary analysis of relationship between dual-task gait and age-related cognitive ability using neural network models

AU - Zhang, Yuying

AU - Xu, Jingying

AU - Nicholas, Afsoun

AU - Arnold, Graham

AU - Wang, Weijie

N1 - Publisher Copyright: © The Author(s) 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

PY - 2026/8/11

Y1 - 2026/8/11

N2 - Background Aging 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. Methods Thirty-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. Results The 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. Conclusion NN 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.

AB - Background Aging 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. Methods Thirty-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. Results The 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. Conclusion NN 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.

KW - age identification

KW - dual-task

KW - kinematics

KW - kinetics

KW - neural networks

KW - spatiotemporal parameters

UR - https://www.scopus.com/pages/publications/105047816535

U2 - 10.1177/09287329261474682

DO - 10.1177/09287329261474682

M3 - Article

SN - 0928-7329

JO - Technology and Health Care

JF - Technology and Health Care

ER -