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

dc.contributorTamber-Rosenau, Benjamin J.
dc.contributor.authorOak, Anushka
dc.description.abstractPerforming 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.
dc.description.departmentPsychology, Department of
dc.description.departmentHonors College
dc.relation.ispartofSummer Undergraduate Research Fellowship
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.titleSearching for Gait Markers of Cognitive-Motor Dual-task Interference using Machine Learning


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