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Classifying Lower Extremity Muscle Fatigue During Walking Using Machine Learning and Inertial Sensors

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Abstract

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.

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Acknowledgments

This research was supported by the NSF (Grant #CBET-0756058) and NSF-Information and Intelligent Systems (IIS) and Smart Health and Wellbeing—1065442 and 1065262. NIOSH (Grant #CDC/NIOSHR01-OH009222). Additionally, supported by the NIH (L30-AG022963-04/NIH HHS/United States).

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Authors and Affiliations

  1. Industrial and Systems Engineering, Virginia Tech, 557 Whittemore Hall, Blacksburg, VA, 24061, USA

    Jian Zhang & Thurmon E. Lockhart

  2. School of Biomedical Engineering and Sciences, Virginia Tech, Wake Forest University, Blacksburg, VA, 24061, USA

    Thurmon E. Lockhart & Rahul Soangra

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  1. Jian Zhang
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Correspondence to Thurmon E. Lockhart.

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Associate Editor Michael R. Torry oversaw the review of this article.

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Zhang, J., Lockhart, T.E. & Soangra, R. Classifying Lower Extremity Muscle Fatigue During Walking Using Machine Learning and Inertial Sensors. Ann Biomed Eng 42, 600–612 (2014). https://doi.org/10.1007/s10439-013-0917-0

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