Genetic Algorithm-Machine Learning Based Classification of Tendon Vibration, Visual, and Cognitive Stimulation Effects on Static Balance

• DOI: 10.2139/ssrn.4555983
• Publication Date: 2023-09-08T16:47:21Z
• Authors: Xueli Ning; Young Kim; Sedong Min; Xin Guo; Jong Gab Ho

Balance training is widely used to improve stability, and Achilles tendon vibration an effective method. However, evaluation of progress often relies on Center Pressure (COP) analysis, which can be challenging for non-experts. To provide objective automated assessment, this study explores machine learning techniques. was applied during standing, COP data were collected under various conditions, including eyes open/closed cognitive/non-cognitive tasks. mean velocity analysis revealed higher velocities in eyes-closed than eyes-open, cognitive non-cognitive tasks, non-vibration conditions. more accurately assess the effects, techniques that combine wavelet decomposition feature extraction. Three genetic algorithm-based models (GA-SVM, GA-LGBM, GA-LR) constructed selection classification. The results showed all three achieved classification accuracies above 80% identifying data, with SVM achieving highest accuracy 89.59%. Among selected features, entropy category features played a crucial role, values conditions This confirms feasibility applying rehabilitation future, discusses influence different sensory inputs performance. identified key also theoretical basis data. These findings valuable insights further optimization balance programs.