A System-Based Approach to Monitoring the Performance of a Human Neuromusculoskeletal System
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Published
Nov 11, 2020
Marcus Mussleman
Deanna H. Gates
Dragan Djurdjanovic
Abstract
This paper presents a system-based method for monitoring a human neuromusculoskeletal (NMS) system. It is based on autoregressive models with exogenous inputs, which link surface electromyographic signals and joint kinematic variables in order to detect changes in system dynamics, as well as to assess joint level and muscle level contributions to those changes. Instantaneous energy and mean frequency of time frequency distributions of electromyographic signals were used as model inputs, while angular velocities of the monitored joints served as outputs. Slow temporal changes in the behavior of the entire system or individual joint models were tracked by analyzing one-step ahead prediction errors of the corresponding models over time. Finally, analysis of the recursively updated models, which tracked the NMS dynamics over time, was used to characterize these changes at the joint and muscular levels. The
methodology is demonstrated on data recorded from 12 human subjects completing a repetitive sawing motion until voluntary exhaustion. Statistically significant decreasing trends in the similarities of the NMS models to those observed in the rested state were observed in all subjects. In addition, decreased joint response to muscle activity, as well as changes in the coordination and motion planning have been detected with all subjects, indicating their fatigue.
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Keywords
Performance Monitoring, Electromyogram (EMG) signals, time-frequency distributions (TFD), neuromusculoskeletal (NMS) system
References
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Asfour, T., Berns, K., Schelling, J. & Dillmann, R. (1999). Programming of manipulation tasks of the humanoid robot ARMAR. Proc. of the 9th International Conference on Advanced Robotics (pages 107-112), Oct. 25-27, 1999, Tokyo, Japan.
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Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. New York City, NY: Oxford University Press.
Buchanan, T. S., Lloyd, D. G., Manal, K. & Besier, T. F. (2005). Estimation of Muscle Forces and Joint Moments Using a Forward-Inverse Dynamics Model. Medicine & Science in Sports & Exercise. 37(11), pages 1911–1916.
Cohen, L. (1995). Time Frequency Analysis. Englewood Cliffs: Prentice-Hall, Inc.
Corcos, D. M., Gottlieb, G. L., Latash, M. L., Almeida, G. L. & Agarwal, G. C. (1992). Electromechanical delay: an experimental artifact,” Journal of
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Côté, J. N., Mathieu, P. A., Levin, M. F. & Feldman, A. G. (2002). Movement reorganization to compensate for fatigue during sawing. Experimental Brain Research. 146(3), pages 394–398.
De Jong, K. A. (1975). Analysis of the behavior of a class of genetic adaptive systems. Doctoral Dissertation, University of Michigan, Ann Arbor, MI, URL: http://deepblue.lib.umich.edu/handle/2027.42/4507
Deep, K., Singh, K. P., Kansal, M. & Mohan, C. (2009). A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation. 212(2), pages 505–518.
Dingwell, J. B., Napolitano, D. F. & Chelidze, D. (2007). A nonlinear approach to tracking slow-time-scale changes in movement kinematics. Journal of Biomechanics. 40(7), pages 1629–1634.
Disselhorst-Klug, C., Schmitz-Rode, T. & Rau, G. (2009). Surface electromyography and muscle force: limits in sEMG-force relationship and new approaches for applications. Clinical Biomechanics. 24(3), pages 225–235.
Edward, M., Bradford, B. & Huston, L. J. (1996). The effects of muscle fatigue on neuromuscular function and anterior tibial translation in healthy knees. The American Journal of Sports Medicine. 24(5), pages 615–621.
Franklin, G., Powell J. & Emami-Naeini, A. (2006). Feedback Control of Dynamic Systems. Upper Saddle River, NJ: Pearson Prentice Hall (5th Edition).
Gates, D. H. & Dingwell, J. B. (2008). The effects of neuromuscular fatigue on task performance during repetitive goal-directed movements. Experimental Brain Research, 187(4), pages 573–585.
Hashemi, J., Hashtrudi-Zaad, K., Morin, E. & Mousavi, P. (2010). Dynamic modeling of EMG-force relationship using parallel cascade identification. Proc. of the IEEE International Conference of the Engineering in Medicine and Biology Society (pages pp. 1328–1331), Aug. 31-Sep. 4, 2010. Buenos Aires, Argentina.
Hurvich, C. M. & Tsai, C.-L. (1991). Bias of the corrected AIC criterion for underfitted regression and time series models, Biometrika, 78(3), pages 499–509.
Huzurbazar, S., Statistics, G. & Foundation, I. (2013). Wavelet-based detection of outliers in time series. Journal of Computational and Graphical Statistics. 11(2). pages 311–327.
Itoh, Y., Akataki, K., Mita, K., Watakabe, M. & Nonaka, H. (2013). Frequency response model of skeletal muscle and its association with contractile properties of skeletal muscle. Journal of Electromyography and Kinesiology. 23(3), pages 572–579.
Jeong J. & Williams, W. J. (1992). Kernel design for reduced interference distributions. IEEE Transactions on Signal Processing. 40(2), pages 402–412.
Keynes, R. D., Aidley, D. J. & Huang, C. L. (2010). Nerve and Muscle. 4th Edition. New York, NY: Cambridge University Press.
Koike, Y. & Kawato, M. (1995) Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biological Cybernetics, 73(4), pages 291–300.
Lee, D., Glueck, M., Khan, A., Fiume, E., & Jackson, K. (2010). A survey of modeling and simulation of skeletal muscle. ACM Transactions on Graphics. 28(4), doi. 10.1145/1559755.1559763
Lindsay, B. G. (1995). Mixture models: theory, geometry and Applications (pages 1-6). Hayward, CA: Institute of Mathematical Statistics.
Manal, K. (2003). A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms, Journal of Biomechanics. 36(8), pages 1197–1202.
Matusita, K. (1955). Decision rules, based on the distance, for problems of fit, two samples, and estimation. The Annals of Mathematical Statistics. 26 pages 631–640.
Montgomery, D. C., Runger G. C. & Hubele, N. F. (2011) Engineering Statistics, John Wiley & Sons (5th Edition).
Myers, J. B., Guskiewicz, K. M., Schneider R. A. & Prentice W. E. (1999). Proprioception and neuromuscular control of the shoulder after muscle fatigue. Journal of Atheletic Training 34(4), pages 362–367.
Pederson, J., Lonn, J., Helstrom, F., Djupsjobacka, M. & Johansson, H. (1999). Localized muscle fatigue decreases the acuity of the movement sense in the human shoulder. Medicine & Science in Sports & Exercise. 31(7), pages 1047–1052.
Potvin J. R. & Brown, S. H. M. (2004). Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates. Journal of Electromyography and Kinesiology. 14(3), pages 389–399.
Segala, D. B., Gates, D. H. Dingwell, J. B. & Chelidze, D. (2011). Nonlinear smooth orthogonal decomposition of kinematic features of sawing reconstructs muscle fatigue evolution as indicated by electromyography. Journal of Biomechanical Engineering. 133(3), doi:10.1115/1.4003320
Song, R. & Tong, K. Y. (2005). Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations. Medical & Biological Engineering & Computing. 43(4), pages 473–480.
van Duinen, H., Renken, R., Maurits, N. & Zijdewind, I. (2007). Effects of motor fatigue on human brain activity, an fMRI study. NeuroImage. 35(4), pages 1438–1449.
Viceconti, M., Testi, D., Taddei, F., Martelli, S., Clapworthy G., & Jan, S. (2006). Biomechanics modeling of the musculoskeletal apparatus: status and key issues. Proceedings of the IEEE. 94(4), pages 725–739.
Westerblad, H. K., Allen D. & Lännergren J. (2002). Muscle fatigue: lactic acid or inorganic phosphate the major cause? Physiology. 17(1), pages 17–21.
Wu, G., van der Helm, F. C. T., Veeger, H. E. J., Makhsous, M., Van Roy, P., Anglin, C., Nagels, J., Karduna, A. R., McQuade, K., Wang, X., Werner, F. W. & Buchholz, B. (2005). ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion - Part II: shoulder, elbow, wrist and hand. Journal of Biomechanics. 38(5), pages 981-992.
Xu, J.X., Zhang, Y. & Pang, Y.J. (2010). A nonlinear parametric identification method for biceps muscle model by using iterative learning approach. Proc. of the IEEE International Conference on Control and Automation (pages 252–257), June 9-11, 2010. Xiamen, China.
Zhou, S., Carey, M. & Snow, R., (1998). Effects of muscle fatigue and temperature on electromechanical delay. Electromyography and Clinical Neurophysiology. 38(2),pages 67–73.
Arslan, Y. Z., Adli, M. A., Akan A. & Baslo, M. B. (2010). Prediction of externally applied forces to human hands using frequency content of surface EMG signals. Computer Methods and Programs in Biomedicine, 98(1), pages 36–44.
Artemiadis P. K. & Kyriakopoulos, K. J. (2009). EMGbased position and force estimates in coupled humanrobot systems: towards EMG-controlled exoskeletons. In Khatib, O., Kumar, V & Pappas, G. (Eds.), Experimental Robotics – The Elevents International Symposium (pages. 241–250). Berlin-Heidelberg: Springer-Verlag.
Asfour, T., Berns, K., Schelling, J. & Dillmann, R. (1999). Programming of manipulation tasks of the humanoid robot ARMAR. Proc. of the 9th International Conference on Advanced Robotics (pages 107-112), Oct. 25-27, 1999, Tokyo, Japan.
Atkinson K. A. (1989). An Introduction to Numerical Analysis. New York, NY, John Wiley & Sons (2nd Edition).
Bäck, T. (1996). Evolutionary Algorithms in Theory and Practice. New York City, NY: Oxford University Press.
Buchanan, T. S., Lloyd, D. G., Manal, K. & Besier, T. F. (2005). Estimation of Muscle Forces and Joint Moments Using a Forward-Inverse Dynamics Model. Medicine & Science in Sports & Exercise. 37(11), pages 1911–1916.
Cohen, L. (1995). Time Frequency Analysis. Englewood Cliffs: Prentice-Hall, Inc.
Corcos, D. M., Gottlieb, G. L., Latash, M. L., Almeida, G. L. & Agarwal, G. C. (1992). Electromechanical delay: an experimental artifact,” Journal of
Electromyography and Kinesiology. 2(2), pages 59–68. Corcos, D. M., Jiang, H.-Y., Wilding, J. & Gottlieb, G. L. (2002). Fatigue induced changes in phasic muscle activation patterns for fast elbow flexion movements. Experimental Brain Research, 142(1), pages 1–12.
Côté, J. N., Mathieu, P. A., Levin, M. F. & Feldman, A. G. (2002). Movement reorganization to compensate for fatigue during sawing. Experimental Brain Research. 146(3), pages 394–398.
De Jong, K. A. (1975). Analysis of the behavior of a class of genetic adaptive systems. Doctoral Dissertation, University of Michigan, Ann Arbor, MI, URL: http://deepblue.lib.umich.edu/handle/2027.42/4507
Deep, K., Singh, K. P., Kansal, M. & Mohan, C. (2009). A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation. 212(2), pages 505–518.
Dingwell, J. B., Napolitano, D. F. & Chelidze, D. (2007). A nonlinear approach to tracking slow-time-scale changes in movement kinematics. Journal of Biomechanics. 40(7), pages 1629–1634.
Disselhorst-Klug, C., Schmitz-Rode, T. & Rau, G. (2009). Surface electromyography and muscle force: limits in sEMG-force relationship and new approaches for applications. Clinical Biomechanics. 24(3), pages 225–235.
Edward, M., Bradford, B. & Huston, L. J. (1996). The effects of muscle fatigue on neuromuscular function and anterior tibial translation in healthy knees. The American Journal of Sports Medicine. 24(5), pages 615–621.
Franklin, G., Powell J. & Emami-Naeini, A. (2006). Feedback Control of Dynamic Systems. Upper Saddle River, NJ: Pearson Prentice Hall (5th Edition).
Gates, D. H. & Dingwell, J. B. (2008). The effects of neuromuscular fatigue on task performance during repetitive goal-directed movements. Experimental Brain Research, 187(4), pages 573–585.
Hashemi, J., Hashtrudi-Zaad, K., Morin, E. & Mousavi, P. (2010). Dynamic modeling of EMG-force relationship using parallel cascade identification. Proc. of the IEEE International Conference of the Engineering in Medicine and Biology Society (pages pp. 1328–1331), Aug. 31-Sep. 4, 2010. Buenos Aires, Argentina.
Hurvich, C. M. & Tsai, C.-L. (1991). Bias of the corrected AIC criterion for underfitted regression and time series models, Biometrika, 78(3), pages 499–509.
Huzurbazar, S., Statistics, G. & Foundation, I. (2013). Wavelet-based detection of outliers in time series. Journal of Computational and Graphical Statistics. 11(2). pages 311–327.
Itoh, Y., Akataki, K., Mita, K., Watakabe, M. & Nonaka, H. (2013). Frequency response model of skeletal muscle and its association with contractile properties of skeletal muscle. Journal of Electromyography and Kinesiology. 23(3), pages 572–579.
Jeong J. & Williams, W. J. (1992). Kernel design for reduced interference distributions. IEEE Transactions on Signal Processing. 40(2), pages 402–412.
Keynes, R. D., Aidley, D. J. & Huang, C. L. (2010). Nerve and Muscle. 4th Edition. New York, NY: Cambridge University Press.
Koike, Y. & Kawato, M. (1995) Estimation of dynamic joint torques and trajectory formation from surface electromyography signals using a neural network model. Biological Cybernetics, 73(4), pages 291–300.
Lee, D., Glueck, M., Khan, A., Fiume, E., & Jackson, K. (2010). A survey of modeling and simulation of skeletal muscle. ACM Transactions on Graphics. 28(4), doi. 10.1145/1559755.1559763
Lindsay, B. G. (1995). Mixture models: theory, geometry and Applications (pages 1-6). Hayward, CA: Institute of Mathematical Statistics.
Manal, K. (2003). A one-parameter neural activation to muscle activation model: estimating isometric joint moments from electromyograms, Journal of Biomechanics. 36(8), pages 1197–1202.
Matusita, K. (1955). Decision rules, based on the distance, for problems of fit, two samples, and estimation. The Annals of Mathematical Statistics. 26 pages 631–640.
Montgomery, D. C., Runger G. C. & Hubele, N. F. (2011) Engineering Statistics, John Wiley & Sons (5th Edition).
Myers, J. B., Guskiewicz, K. M., Schneider R. A. & Prentice W. E. (1999). Proprioception and neuromuscular control of the shoulder after muscle fatigue. Journal of Atheletic Training 34(4), pages 362–367.
Pederson, J., Lonn, J., Helstrom, F., Djupsjobacka, M. & Johansson, H. (1999). Localized muscle fatigue decreases the acuity of the movement sense in the human shoulder. Medicine & Science in Sports & Exercise. 31(7), pages 1047–1052.
Potvin J. R. & Brown, S. H. M. (2004). Less is more: high pass filtering, to remove up to 99% of the surface EMG signal power, improves EMG-based biceps brachii muscle force estimates. Journal of Electromyography and Kinesiology. 14(3), pages 389–399.
Segala, D. B., Gates, D. H. Dingwell, J. B. & Chelidze, D. (2011). Nonlinear smooth orthogonal decomposition of kinematic features of sawing reconstructs muscle fatigue evolution as indicated by electromyography. Journal of Biomechanical Engineering. 133(3), doi:10.1115/1.4003320
Song, R. & Tong, K. Y. (2005). Using recurrent artificial neural network model to estimate voluntary elbow torque in dynamic situations. Medical & Biological Engineering & Computing. 43(4), pages 473–480.
van Duinen, H., Renken, R., Maurits, N. & Zijdewind, I. (2007). Effects of motor fatigue on human brain activity, an fMRI study. NeuroImage. 35(4), pages 1438–1449.
Viceconti, M., Testi, D., Taddei, F., Martelli, S., Clapworthy G., & Jan, S. (2006). Biomechanics modeling of the musculoskeletal apparatus: status and key issues. Proceedings of the IEEE. 94(4), pages 725–739.
Westerblad, H. K., Allen D. & Lännergren J. (2002). Muscle fatigue: lactic acid or inorganic phosphate the major cause? Physiology. 17(1), pages 17–21.
Wu, G., van der Helm, F. C. T., Veeger, H. E. J., Makhsous, M., Van Roy, P., Anglin, C., Nagels, J., Karduna, A. R., McQuade, K., Wang, X., Werner, F. W. & Buchholz, B. (2005). ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion - Part II: shoulder, elbow, wrist and hand. Journal of Biomechanics. 38(5), pages 981-992.
Xu, J.X., Zhang, Y. & Pang, Y.J. (2010). A nonlinear parametric identification method for biceps muscle model by using iterative learning approach. Proc. of the IEEE International Conference on Control and Automation (pages 252–257), June 9-11, 2010. Xiamen, China.
Zhou, S., Carey, M. & Snow, R., (1998). Effects of muscle fatigue and temperature on electromechanical delay. Electromyography and Clinical Neurophysiology. 38(2),pages 67–73.
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