The Effect of Biomechanical Features on Classification of Dual-Task Gait

Early detection of Alzheimer’s disease and related disorders (ADRDs) has been a focus of research with the hope that early intervention may improve clinical outcomes. The manifestation of motor impairment in the early stages of ADRD has led to the inclusion of gait assessments including spatiotemporal parameters in clinical evaluations. This study aims to determine the effect of adding kinetic and kinematic gait features to the classification of different levels of cognitive load in healthy individuals. A dual-task paradigm was used to simulate cognitive impairment in 40 healthy adults, with single-task walking trials representing normal, healthy gait. The Paced Auditory Serial Addition Task (PASAT) was administered at two different interstimulus intervals (ISIs) representing two levels of cognitive load in dual-task gait. We predicted that a richer dataset would improve classification accuracy relative to spatiotemporal parameters. Repeated measures analysis of variance (ANOVA) showed significant changes in 15 different gait features across all three levels of cognitive load. We used three supervised machine learning algorithms to classify data points using a series of different gait feature sets with performance based on the area under the curve (AUC). Classification yielded 0.778 AUC across all three conditions (0.889 AUC Single versus Dual) using kinematic and spatiotemporal features compared to 0.724 AUC using spatiotemporal features only (0.792 AUC Single versus Dual). These data suggest that additional kinematic parameters improve classification performance. However, the benefit of measuring a wider set of parameters compared to their cost needs consideration. Further work will lead to a clinically viable ADRD detection classifier.