Investigation into machine learning with impedance SHM for damage detection and classification within simulated total knee replacements

Total knee arthroplasty (TKA) is currently one of the most common orthopedic surgeries worldwide. While TKA is generally successful, revision due to pain and failure is inevitable and adversely impacts patient outcomes. Catastrophic failure rates may be reduced by early detection of damage. This work evaluates the ability of machine learning to detect and classify damage from piezoelectric impedance measurements of total knee replacements (TKRs). Multiple simulated TKR test samples are constructed and artificially damaged to varying degrees, and impedance spectra are recorded. Five machine learning architectures are examined including decision trees, k-nearest neighbor (KNN) classifiers, discriminant analysis classifiers, naive Bayes classifiers, and support vector machines (SVMs). Hyperparameters for each architecture are first manually tuned. Next, the best-performing model of each architecture is retrained on an impedance bandwidth subset in attempt to reduce dimensionality required for classification. Finally, Bayesian optimization is performed on each architecture to determine the best-performing hyperparameters. Results demonstrate that lower-frequency bandwidths are most sensitive to damage. The best-performing optimized model is a KNN model that attains an accuracy of 76.67%, an average AUC score of 81.85%, an overall performance score of 80.03%, as well as macro-recall and macro-F1 scores of 75.33% and 75%, respectively.