Age-Related Differences in Healthy Adults Walking Patterns Under a Cognitive Task With Deep Neural Networks

O. Costilla-Reyes, Patricia Scully, Iracema Leroi, Krikor Ozanyan

Research output: Contribution to journalArticlepeer-review

Abstract

We investigated cognitively demanding tasks on patterns of human gait in healthy adults with a deep learning methodology that learns from raw gait data. Age-related differences were analyzed in dual-tasks in a cohort of 69 cognitively healthy adults organized in stratified groups by age. A novel spatio-temporal deep learning methodology was introduced to effectively classify dual-tasks from spatio-temporal raw gait data, obtained from a unique tomography floor sensor. The approach outperformed traditional machine learning approaches. The most favorable classification F-score obtained was of 97.3% in dual-tasks in a young age group experiment for a total of 12 users. The deep machine learning methodology outperformed classical machine learning methodologies by 63.5% in the most favorable case. Finally, a 2D manifold representation was obtained from trained deep learning models' data, to visualize and identify clusters from features learned by the deep learning models. This study demonstrates a novel approach to dual-task research by proposing a data-driven learning methodology with stratified age-groups.
Original languageEnglish
Pages (from-to)2353-2363
Number of pages11
JournalIEEE Sensors Journal
Volume21
Issue number2
DOIs
Publication statusPublished - 2 Sept 2020

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