Medical & Biological Engineering & Computing

, Volume 57, Issue 6, pp 1199–1211 | Cite as

Mechanomyography-based muscle fatigue detection during electrically elicited cycling in patients with spinal cord injury

  • Jannatul Naeem
  • Nur Azah HamzaidEmail author
  • Md. Anamul Islam
  • Amelia Wong Azman
  • Manfred Bijak
Original Article


Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI performed FES cycling exercises for 30 min. MMG signals were recorded on the quadriceps muscle group (rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM)) and categorized into non-fatigued and fatigued muscle contractions for the first and last 10 min of the cycling session. For each subject, a total of 1800 contraction-related MMG signals were used to train the SVM classifier and another 300 signals were used for testing. The average classification accuracy (4-fold) of non-fatigued and fatigued state was 90.7% using MFCC feature, 74.5% using root mean square (RMS), and 88.8% with combined MFCC and RMS features. Inter-subject prediction accuracy suggested training and testing data to be based on a particular subject or large collection of subjects to improve fatigue prediction capacity.

Graphical abstract


Functional electrical stimulation Muscle fatigue Spinal cord injury Mechanomyography Mel frequency cepstral coefficients (MFCC) 



The study was financially supported by the University of Malaya Research Grant (UMRG), grant no. RP035A-15HTM.

Compliance with ethical standards

This study was granted by the University of Malaya Research Ethics Committee (approval no: 1003.14 (1)).


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Copyright information

© International Federation for Medical and Biological Engineering 2019

Authors and Affiliations

  • Jannatul Naeem
    • 1
  • Nur Azah Hamzaid
    • 1
    Email author
  • Md. Anamul Islam
    • 1
  • Amelia Wong Azman
    • 2
  • Manfred Bijak
    • 1
    • 3
  1. 1.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Electrical and Computer Engineering, Faculty of EngineeringInternational Islamic University MalaysiaKuala LumpurMalaysia
  3. 3.Center for Medical Physics and Biomedical EngineeringMedical University ViennaViennaAustria

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