Open Access

ARTICLE

Timing and Classification of Patellofemoral Osteoarthritis Patients Using Fast Large Margin Classifier

Mai Ramadan Ibraheem¹, Jilan Adel², Alaa Eldin Balbaa³, Shaker El-Sappagh⁴, Tamer Abuhmed^5,*, Mohammed Elmogy⁶

1 Faculty of Computers and Information, Kafrelsheikh University, Egypt
2 Faculty of Physical Therapy, Kafrelsheikh University, Egypt
3 Faculty of Physical Therapy, Nahda University, NUB, Egypt
4 Universidade de Santiago de Compostela, Santiago de Compostela, Spain
5 College of Computing, Sungkyunkwan University, Seoul, 06351, Korea
6 Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt

* Corresponding Author: Tamer Abuhmed. Email:

(This article belongs to this Special Issue: Artificial Intelligence and IoT based intelligent systems using high performance computing for Medical applications.)

Computers, Materials & Continua 2021, 67(1), 393-409. https://doi.org/10.32604/cmc.2021.014446

Received 21 September 2020; Accepted 03 November 2020; Issue published 12 January 2021

Abstract

Surface electromyogram (sEMG) processing and classification can assist neurophysiological standardization and evaluation and provide habitational detection. The timing of muscle activation is critical in determining various medical conditions when looking at sEMG signals. Understanding muscle activation timing allows identification of muscle locations and feature validation for precise modeling. This work aims to develop a predictive model to investigate and interpret Patellofemoral (PF) osteoarthritis based on features extracted from the sEMG signal using pattern classification. To this end, sEMG signals were acquired from five core muscles over about 200 reads from healthy adult patients while they were going upstairs. Onset, offset, and time duration for the Transversus Abdominus (TrA), Vastus Medialis Obliquus (VMO), Gluteus Medius (GM), Vastus Lateralis (VL), and Multifidus Muscles (ML) were acquired to construct a classification model. The proposed classification model investigates function mapping from real-time space to a PF osteoarthritis discriminative feature space. The activation feature space of muscle timing is used to train several large margin classifiers to modulate muscle activations and account for such activation measurements. The fast large margin classifier achieved higher performance and faster convergence than support vector machines (SVMs) and other state-of-the-art classifiers. The proposed sEMG classification framework achieved an average accuracy of 98.8% after 7 s training time, improving other classification techniques in previous literature.

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