Towards a Diagnostic Tool for Diagnosing Joint Pathologies: Supervised Learning of Acoustic Emission Signals

Khadijat A Olorunlambe, Zhe Hua, Duncan E T Shepherd, Karl D Dearn

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Abstract

Acoustic emission (AE) testing detects the onset and progression of mechanical flaws. AE as a diagnostic tool is gaining traction for providing a tribological assessment of human joints and orthopaedic implants. There is potential for using AE as a tool for diagnosing joint pathologies such as osteoarthritis and implant failure, but the signal analysis must differentiate between wear mechanisms-a challenging problem! In this study, we use supervised learning to classify AE signals from adhesive and abrasive wear under controlled joint conditions. Uncorrelated AE features were derived using principal component analysis and classified using three methods, logistic regression, k-nearest neighbours (KNN), and back propagation (BP) neural network. The BP network performed best, with a classification accuracy of 98%, representing an exciting development for the clustering and supervised classification of AE signals as a bio-tribological diagnostic tool.

Original languageEnglish
Article number8091
JournalSensors
Volume21
Issue number23
DOIs
Publication statusPublished - 3 Dec 2021

Keywords

  • Acoustics
  • Humans
  • Neural Networks, Computer
  • Osteoarthritis/diagnosis
  • Principal Component Analysis
  • Supervised Machine Learning

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