System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement



Published Oct 26, 2023
Samuel Bertelson Lindsey Molina Richard Neptune Dragan Djurdjanovic


Physical fatigue accounts for many injuries in the workplace, sports arena, or battlefield. The traditional approaches to monitor fatigue rely on detecting and measuring shifts in the person’s muscular surface electromyography (sEMG) signals. However, assessing neuromuscular fatigue based purely on sEMG signals fails to account for the changing muscle dynamics during long dynamic physical tasks. To combat this dilemma, a system-based methodology has been recently developed and applied to several upper-extremity tasks. In this paper, we validate the efficacy of this novel methodology on the lower extremities during a dynamic activity. Specifically, the system-based monitoring methodology was applied to a cycling endurance task. It was statistically demonstrated that the system-based methodology resulted in a more-sensitive and less noisy metric, in comparison with an EMG-based methodology. The efficacy of the methodology was further illustrated by analyzing the inter-segmental recovering and fatiguing trends, which aligned with each muscle’s expected inter-muscle synergistic relationship.

How to Cite

Bertelson, S., Molina, L., Neptune, R., & Djurdjanovic, D. (2023). System-based Monitoring of Muscular Fatigue in Lower-Extremity Movement. Annual Conference of the PHM Society, 15(1).
Abstract 151 | PDF Downloads 99



fatigue, data-driven modeling, biomechanics, sEMG

Antwi-Afari, M. F., Li, H., Edwards, D. J., Pärn, E. A., Seo, J., & Wong, A. Y. L. (2017). Biomechanical analysis of risk factors for work-related musculoskeletal disorders during repetitive lifting task in construction workers. Automation in Construction, 83, 41–47.
Arendt-Nielsen, L., & Mills, K. R. (1985). The relationship between mean power frequency of the EMG spectrum and muscle fibre conduction velocity. Electroencephalography and clinical Neurophysiology, 60(2), 130-134.
Bleakie, A., & Djurdjanovic, D. (2016). Growing structure multiple model system for quality estimation in manufacturing processes. IEEE Transactions on Semiconductor Manufacturing, 29(2), 79–97.
Boyas, S., & Guével, A. (2011). Neuromuscular fatigue in healthy muscle: Underlying factors and adaptation mechanisms. Annals of Physical and Rehabilitation Medicine, 54(2), 88–108.
Choi, H. I., & Williams, W. J. (1989). Improved time-frequency representation of multicomponent signals using exponential kernels. IEEE Transactions on Acoustics, Speech, and Signal Processing, 37(6), 862-871.
Cholette, M. E., & Djurdjanovic, D. (2012). Precedent-free fault isolation in a diesel engine exhaust gas recirculation system. Journal of Dynamic Systems, Measurement, and Control, 134(3).
Clark, D. J., Ting, L. H., Zajac, F. E., Neptune, R. R., & Kautz, S. A. (2010). Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke. Journal of neurophysiology, 103(2), 844-857.
Cohen, L. (1995). Time Frequency Analysis. Prentice Hall.
Collecting Experimental Data - OpenSim Documentation - Global Site. (n.d.).
d’Avella, A., Saltiel, P., & Bizzi, E. (2003). Combinations of muscle synergies in the construction of a natural motor behavior. Nature Neuroscience, 6(3), 300–308.
Delp, S. L., Anderson, F. C., Arnold, A. S., Loan, P., Habib, A., John, C. T., Guendelman, E., & Thelen, D. G. (2007). OpenSim: Open-source software to create and analyze dynamic simulations of movement. IEEE Transactions on Biomedical Engineering, 54(11), 1940–1950.
Dindorf, C., Bartaguiz, E., Dully, J., Sprenger, M., Becker, S., Fröhlich, M., Ludwig, O. (2023). In vivo monitoring of acute and intermittent fatigue in sport climbing using near-infrared spectroscopy wearable biosensors. Sports, 11(2), 37.
Enoka, R. M., & Duchateau, J. (2008). Muscle fatigue: What, why and how it influences muscle function. The Journal of Physiology, 586(1), 11–23.
Gefen, A. (2002). Biomechanical analysis of fatigue-related foot injury mechanisms in athletes and recruits during Intensive Marching. Medical Biological Engineering Computing, 40(3), 302–310.
Georgakis, A., Stergioulas, L. K., & Giakas, G. (2003). Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency. IEEE transactions on biomedical engineering, 50(2), 262-265.
Hill, A. V. (1938). The heat of shortening and the dynamic constants of Muscle. Proceedings of the Royal Society of London. Series B - Biological Sciences, 126(843), 136–195.
Jones, A. M., Grassi, B., Christensen, P. M., Krustrup, P., Bangsbo, J., & Poole, D. C. (2011). Slow component of V˙O2 Kinetics. Medicine & Science in Sports & Exercise, 43(11), 2046–2062.
Kendall, M. G. (1975). Rank correlation methods.
Liu, J., Djurdjanovic, D., Marko, K., & Ni, J. (2009). Growing structure multiple model systems for anomaly detection and fault diagnosis. Journal of Dynamic Systems, Measurement, and Control, 131(5).
Madden, K. E., Djurdjanovic, D., Deshpande, A. D. (2021). Using a system-based monitoring paradigm to assess fatigue during submaximal static exercise of the elbow extensor muscles. Sensors, 21(4), 1024.
Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the econometric society, 245-259.
Matusita, K. (1967, January). Classification based on distance in multivariate Gaussian cases. In Proc. 5th. Berkeley Symp. Math. Stat. Probab. vol (Vol. 1, pp. 299-304).
McDonald, A. C., Mulla, D. M., & Keir, P. J. (2019). Using EMG amplitude and frequency to calculate a multimuscle fatigue score and evaluate global shoulder fatigue. Human Factors, 61(4), 526-536.
Millard, M., Uchida, T., Seth, A., & Delp, S. L. (2013). Flexing computational muscle: Modeling and simulation of Musculotendon Dynamics. Journal of Biomechanical Engineering, 135(2).
Musselman, M., Gates, D., & Djurdjanovic, D. (2017). System based monitoring of a neuromusculoskeletal system using divide and conquer type models. 2017 IEEE Aerospace Conference.
Neptune, R. R., & Hull, M. L. (1998). Evaluation of performance criteria for simulation of submaximal steady-state cycling using a forward dynamic model. Journal of Biomechanical Engineering, 120(3), 334–341.
Newmiller, J., Hull, M. L., & Zajac, F. E. (1988). A mechanically decoupled two force component bicycle pedal dynamometer. Journal of Biomechanics, 21(5), 375-386.
Nybo, L. (2003). CNS fatigue and prolonged exercise: Effect of glucose supplementation. Medicine & Science in Sports & Exercise, 35(4), 589–594.
Ou, H., Gates, D., Johnson, S., & Djurdjanovic, D. (2022). Model-based fusion of surface electromyography with kinematic and kinetic measurements for monitoring of muscle fatigue. International Journal of Prognostics and Health Management, 13(2).
Patel, D., Tiwari, R., Pandey, S., Nikam, R. (2020). Real-time fatigue detection system using computer vision. International Journal of Engineering Research And, V9(06).
Petrofsky, J. S., Phillips, C. A., Sawka, M. N., Hanpeter, D., Lind, A. R., & Stafford, D. (1981). Muscle fiber recruitment and blood pressure response to isometric exercise. Journal of Applied Physiology, 50(1), 32–37.
San-Millán, I., Hill, J. C., & Calleja-González, J. (2020). Indirect assessment of skeletal muscle glycogen content in professional soccer players before and after a match through a non-invasive ultrasound technology. Nutrients, 12(4), 971.
U.S. Bureau of Labor Statistics. (2016, November 10). Nonfatal occupational injuries and illnesses requiring days away from work. U.S. Bureau of Labor Statistics.
Vøllestad, N. K. (1997). Measurement of human muscle fatigue. Journal of Neuroscience Methods, 74(2), 219–227.
Zhang, J., Lockhart, T. E., Soangra, R. (2013). Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors. Annals of Biomedical Engineering, 42(3), 600–612.
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