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

• Published in: IEEE sensors journal 2021-01, Vol.21 (2), p.2353-2363
• Main Authors: Costilla-Reyes, Omar, Scully, Patricia, Leroi, Iracema, Ozanyan, Krikor B.
• Format: Article
• Language: English
• Subjects: Adults; Age; age-related differences; Artificial neural networks; Cognitive tasks; Data models; Deep learning; dual task; floor sensor system; Gait; Legged locomotion; Machine learning; Methodology; Sensor systems; Task analysis; Two dimensional models; Walking
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2020.3021349

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.