Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors

Jian Zhang, Thurmon E. Lockhart, Rahul Soangra

Research output: Contribution to journalArticlepeer-review

103 Scopus citations


Fatigue in lower extremity musculature is associated with decline in postural stability, motor performance and alters normal walking patterns in human subjects. Automated recognition of lower extremity muscle fatigue condition may be advantageous in early detection of fall and injury risks. Supervised machine learning methods such as support vector machines (SVMs) have been previously used for classifying healthy and pathological gait patterns and also for separating old and young gait patterns. In this study we explore the classification potential of SVM in recognition of gait patterns utilizing an inertial measurement unit associated with lower extremity muscular fatigue. Both kinematic and kinetic gait patterns of 17 participants (29 ± 11 years) were recorded and analyzed in normal and fatigued state of walking. Lower extremities were fatigued by performance of a squatting exercise until the participants reached 60% of their baseline maximal voluntary exertion level. Feature selection methods were used to classify fatigue and no-fatigue conditions based on temporal and frequency information of the signals. Additionally, influences of three different kernel schemes (i.e., linear, polynomial, and radial basis function) were investigated for SVM classification. The results indicated that lower extremity muscle fatigue condition influenced gait and loading responses. In terms of the SVM classification results, an accuracy of 96% was reached in distinguishing the two gait patterns (fatigue and no-fatigue) within the same subject using the kinematic, time and frequency domain features. It is also found that linear kernel and RBF kernel were equally good to identify intra-individual fatigue characteristics. These results suggest that intra-subject fatigue classification using gait patterns from an inertial sensor holds considerable potential in identifying "at-risk" gait due to muscle fatigue.

Original languageEnglish (US)
Pages (from-to)600-612
Number of pages13
JournalAnnals of Biomedical Engineering
Issue number3
StatePublished - Mar 2014
Externally publishedYes


  • Falls
  • Fatigue
  • Gait
  • Jerk cost
  • Machine learning
  • Support vector machines

ASJC Scopus subject areas

  • Biomedical Engineering


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