Comparative Analysis of Machine Learning Techniques for the Classification of Knee Abnormality

Ankit Vijayvargiya, Rajesh Kumar, Nilanjan Dey, Joao Manuel R.S. Tavares

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)

Abstract

Knee abnormality is a major problem in elderly people these days. It can be diagnosed by using Magnetic Resonance Imaging (MRI) or X-Ray imaging techniques. X-Ray is only used for primary evaluation, while MRI is an efficient way to diagnose knee abnormality, but it is very expensive. In this work, Surface EMG (sEMG) signals acquired from healthy and knee abnormal individuals during three different lower limb movements: Gait, Standing and Sitting, were used for classification. Hence, first Discrete Wavelet Transform (DWT) was used for denoising the input signals; then, eleven different time-domain features were extracted by using a 256 msec windowing with 25% of overlapping. After that, the features were normalized between 0 (zero) to 1 (one) and then selected by using the backward elimination method based on the p-value test. Five different machine learning classifiers: K-nearest neighbor, support vector machine, decision tree, random forest and extra tree, were studied for the classification step. Our result shows that the Extra Tree Classifier with ten cross-validations gave the highest accuracy (91%) in detecting knee abnormality from the sEMG signals under analysis.

Original languageEnglish
Title of host publication2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
ISBN (Electronic)9781728163246
DOIs
Publication statusPublished - 30 Oct 2020
Externally publishedYes
Event5th IEEE International Conference on Computing Communication and Automation, ICCCA 2020 - Greater Noida, India
Duration: 30 Oct 202031 Oct 2020

Publication series

Name2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020

Conference

Conference5th IEEE International Conference on Computing Communication and Automation, ICCCA 2020
Country/TerritoryIndia
CityGreater Noida
Period30/10/2031/10/20

Keywords

  • Discrete Wavelet Transform (DWT)
  • Knee Abnormality
  • Machine Learning Classifiers
  • Surface Electromyography (sEMG)

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Control and Optimization

Fingerprint

Dive into the research topics of 'Comparative Analysis of Machine Learning Techniques for the Classification of Knee Abnormality'. Together they form a unique fingerprint.

Cite this