Classification of Older Adults Undergoing Two Dual-Task Training Protocols Based on Artificial Intelligent Methods

Abstract

Activities of daily living require efficient management between motor and cognitive tasks, known as dual tasks. The ability to properly perform such activities progressively decreases with aging. Hence, there is much effort in developing methods to identify relevant features and attenuate this functional loss. This study aimed at testing the ability of some intelligent algorithms to identify and differentiate functional features in community-dwelling older adults undergoing two 24-weeks dual-task training protocols. Additionally, we intended to provide a clear breakdown of the functional performance of the participants after undergoing two types of dual-task training protocols. We utilized the database from the EQUIDOSO-I clinical trial as input to four different types of intelligent classifiers. The algorithm's performances were analysed considering accuracy, precision, sensitivity, and specificity. Individually, SVM achieved a higher value for sensitivity for one population group, but the remaining metrics continued to report low metrics indicating poor generalization ability of the model. Nonetheless, AdaBoost presented the most consistent results at the end of the intervention period (T2). No between-group difference affected the models' accomplishments, corroborated by the similar performances at the baseline (T1) and the 6-month follow-up (T3). Therefore, future works should focus on establishing distinct protocols to enhance functional aspects of older adults.