Exploring Achilles Tendon Vibration Data Classification for Balance Training: A Wavelet-Based Machine Learning Approach

Abstract

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

Keywords

• Balance
• Achilles tendon vibration training
• machine-learning
• genetic algorithm
• feature selection
• wavelet discrete decomposition