Searching for Gait Markers of Cognitive-Motor Dual-task Interference using Machine Learning



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Performing two concurrent tasks, or dual-tasking (DT), commonly results in reduced performance especially when compared to single tasking (ST). Two kinds of DT have been studied in largely separate literatures: concurrent cognitive tasks (cognitive-cognitive: CC) and concurrent cognitive task and motor tasks (cognitive-motor: CM). The present project’s long-term goal is to serve as a proof-of-principal for future studies to relate CC and CM DT in at-risk populations for high DT costs and consequences (e.g. falls in the elderly). The project’s immediate goal is to develop a more sensitive measure for CM DT. Healthy young adult participants (N=9) walked at a set pace, performed a demanding cognitive task (serial sevens subtraction), or performed both tasks concurrently. Motor behavior was tracked using a pressure-sensitive treadmill and 3D motion tracking. These data were reconstructed using Vicon Nexus software and custom preprocessing with MATLAB, iteratively divided into training/testing sets, and submitted to a linear support vector machine learning classifier. All classification was performed within participants. Though a t-test did not result in statistical significance for the group, more sensitive within-participant permutation tests revealed significant above-chance classification in 7 of 9 participants. This result indicates that 3D motion parameters can support detection of CM DT from gait at a constant walking speed. Future analyses will include refined preprocessing, alternative machine learning approaches, and classification based on higher-order features of the data. Support vector machine feature weights will also be analyzed to identify informative/uninformative features, potentially allowing streamlining of future data collection.