A hybrid linear discriminant analysis and genetic algorithm to create a linear model of aging when performing motor tasks through inertial sensors positioned on the hand and forearm

• DOI: 10.1186/s12938-023-01161-4
• Publication Date: 2023-10-16T19:01:51Z
• Authors: Veronica de Lima ...; Caio Tonus Ribeiro; Guilherme Lopes C...; Maria José Ferrei...; Daniel Hilário da...; Selma Terezinha M...; Adriano de Olivei...; Adriano Alves Per...
• Keywords: Aging; Artificial intelligence; Health Professions; FOS: Mechanical engineering; Pattern recognition (psychology); Bayesian multivariate linear regression; Engineering; Cognition; Sociology; Pathology; Psychology; Linear regression; Statistics; Discriminant Analysis; Life Sciences; Mechanical engineering; Correlation; FOS: Sociology; FOS: Psychology; Algorithm; Forearm; Analysis of Electromyography Signal Processing; Physical Sciences; Medicine; Algorithms; Linear discriminant analysis; LDA; Cognitive Neuroscience; Linear model; Biomedical Engineering; Geometry; Physical Therapy, Sports Therapy and Rehabilitation; Inertial sensors; FOS: Medical engineering; Health Sciences; Medical technology; FOS: Mathematics; Humans; R855-855.5; Aged; Demography; Sensory Feedback; Gait Analysis and Fall Prevention in Elderly; Research; General linear model; Sensorimotor Learning; Computer science; Musculoskeletal Modeling; Linear motor; Computational Principles of Motor Control and Learning; Physical medicine and rehabilitation; Linear Models; Age groups; Mathematics; Neuroscience

Abstract Background During the aging process, cognitive functions and performance of the muscular and neural system show signs of decline, thus making the elderly more susceptible to disease and death. These alterations, which occur with advanced age, affect functional performance in both the lower and upper members, and consequently human motor functions. Objective measurements are important tools to help understand and characterize the dysfunctions and limitations that occur due to neuromuscular changes related to advancing age. Therefore, the objective of this study is to attest to the difference between groups of young and old individuals through manual movements and whether the combination of features can produce a linear correlation concerning the different age groups.

Methods This study counted on 99 participants, these were divided into 8 groups, which were grouped by age. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Firstly, the participants were divided into groups of young and elderly to verify if the groups could be distinguished through the features alone. Following this, the features were combined using the linear discriminant analysis (LDA), which gave rise to a singular feature called the LDA-value that aided in verifying the correlation between the different age ranges and the LDA-value.

Results The results demonstrated that 125 features are able to distinguish the difference between the groups of young and elderly individuals. The use of the LDA-value allows for the obtaining of a linear model of the changes that occur with aging in the performance of tasks in line with advancing age, the correlation obtained, using Pearson’s coefficient, was 0.86.

Conclusion When we compare only the young and elderly groups, the results indicate that there is a difference in the way tasks are performed between young and elderly individuals. When the 8 groups were analyzed, the linear correlation obtained was strong, with the LDA-value being effective in obtaining a linear correlation of the eight groups, demonstrating that although the features alone do not demonstrate gradual changes as a function of age, their combination established these changes.