근육 파라미터 최적화를 통한 발목관절 모멘트 추정 모델 개발 및 평가
Development and evaluation of estimation model of ankle joint moment from optimization of muscle parameters
0
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
Estimation of muscle forces is important in biomechanics, therefore many researchers have tried to build a muscle model. Recently, optimization techniques for adjusting muscle parameters, i.e. EMG-driven model, have been used to estimate muscle forces and predict joint moments. In this study, an EMG-driven model based on the previous studies has been developed and isometric and isokinetic contraction movements were evaluated to validate the developed model. One healthy male participated in this study. The dynamometer tasks were performed for maximum voluntary isometric contractions (MVIC) for ankle dorsi/plantarflexors, isokinetic contraction at both 30°/s and 60°/s. EMGs were recorded from the tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis and soleus muscles at the sampling rate of 1000 Hz. The MVIC trial was used to customize the EMG-driven model to the specific subject. Once the subject's own model was developed, the model was used to predict the ankle joint moment for the other two dynamic movements. When no optimization was applied to characterize the muscle parameters, weak correlations were observed between the model prediction and the measured joint moment with large RMS error over 100% (r = 0.468 (123%) and r = 0.060 (159%) in 30°/s and 60°/s dynamic movements, respectively). However, once optimization was applied to adjust the muscle parameters, the predicted joint moment was highly similar to the measured joint moment with relatively small RMS error below 40% (r = 0.955 (21%) and r = 0.819 (36%) and in 30°/s and 60°/s dynamic movements, respectively). We expect that our EMG-driven model will be employed in our future efforts to estimate muscle forces of the elderly.
Abstract
Estimation of muscle forces is important in biomechanics, therefore many researchers have tried to build a muscle model. Recently, optimization techniques for adjusting muscle parameters, i.e. EMG-driven model, have been used to estimate muscle forces and predict joint moments. In this study, an EMG-driven model based on the previous studies has been developed and isometric and isokinetic contraction movements were evaluated to validate the developed model. One healthy male participated in this study. The dynamometer tasks were performed for maximum voluntary isometric contractions (MVIC) for ankle dorsi/plantarflexors, isokinetic contraction at both 30°/s and 60°/s. EMGs were recorded from the tibialis anterior, gastrocnemius medialis, gastrocnemius lateralis and soleus muscles at the sampling rate of 1000 Hz. The MVIC trial was used to customize the EMG-driven model to the specific subject. Once the subject's own model was developed, the model was used to predict the ankle joint moment for the other two dynamic movements. When no optimization was applied to characterize the muscle parameters, weak correlations were observed between the model prediction and the measured joint moment with large RMS error over 100% (r = 0.468 (123%) and r = 0.060 (159%) in 30°/s and 60°/s dynamic movements, respectively). However, once optimization was applied to adjust the muscle parameters, the predicted joint moment was highly similar to the measured joint moment with relatively small RMS error below 40% (r = 0.955 (21%) and r = 0.819 (36%) and in 30°/s and 60°/s dynamic movements, respectively). We expect that our EMG-driven model will be employed in our future efforts to estimate muscle forces of the elderly.
참고문헌 (24)
- M.G. Hoy, "A musculoskeletal model of the human lower extremity:the effect of muscle,tendon,and moment arm on the moment-angle relationship", J.Biomech., 23, pp.157~169 (1990)
- F.E.Zajac, "Muscle and tendon:properties,models,scaling,and application to biomechanics and motor control", CRC Crit.Rev.Biomed.Eng., 17, pp.359~411 (1989)
- M.S.Lisa, "Using musculoskeletal models to explore strategies for improving performance in electrical stimulation-induced leg cycle ergometry", 박사, pp.1992
- S.L. Delp, "An interactive graphics-based model of the lower extremity to study orthopaedic surgical procedures", IEEE T.Biomed.Eng., 37, pp.757~767 (1990)
- S.L.Delp, "Surgery simulation:A computer graphics system to analyze and design musculoskeletal reconstructions of the lower limb", 박사, pp.1990
- R.A. Brand, "A model of lower extremity muscular anatomy", J.Biomech.Eng., 104, pp.304~310 (1982)
- R.A. Brand, "The sensitivity of muscle force predictions to changes in physiologic Cross-Sectional Area", J.Biomech., 19, pp.589~596 (1986)
- J.A.Friederich, "Muscle fiber architecture in human lower limb", J.Biomech., 23, pp.91~95 (1990)
- T.L. Wickiewicz, "Muscle architecture of the human lower limb", Clin.Orthop.Relat.R., 179, pp.275~283 (1983)
- T.S. Buchanan, "Neuromusculoskeletal Modeling:Estimation of Muscle Forces and Joint Moments and Movements From Measurements of Neural Command", J.Appl.Biomech., 20, pp.367~395 (2004)
- P. Aagaard, "Isokinetic muscle strength and capacity for muscular knee joint stabilization in elite sailors", Int.J.Sport.Med., 18, pp.521~525 (1997)
- K.W. Hayes, "Differential muscle strength decline in osteoarthritis of the knee.A developing hypothesis", Arthrit.Care Res., 5, pp.24~28 (1992)
- M.T. Read, "Comparison of hamstring", quadriceps isokinetic strength ratios and power in tennis,squash and track athletes, 1990, pp.24, 178~182
- W. Herzog, "Moment-length relations of rectus femoris muscles of speed skaters", cyclists and runners, 1991, pp.23, 1289~1296
- D.G. Lloyd, "An EMG-driven musculoskeletal model to estimate muscle forces and knee joint moments in vivo", J.Biomech., 36, pp.765~776 (2003)
- A.C. Guimaraes, "The EMG-force relationship of the cat soleus muscle and its association with contractile conditions during locomotion", J.Exp.Biol., 1995, pp.975~987 (1995)
- H.S. Milner-Brown, "Changes in firing rate of human motor units during linearly changing voluntary contractions", J.Phys., 230, pp.371~390 (1973)
- J.J. Woods, "Linear and nonlinear surface EMG", force relationships in human muscles.An anatomical, Am.J.Phys.Med., pp.1983, 62[DOI]
- K. Manal, "A one-parameter neural activation to muscle activation model:estimating isometric joint moments from electromyograms", J.Biomech., 36, pp.1197~1202 (2003)
- J.M. Bland, "Statistical method for assessing agreement between two methods of clinical measurement", The Lancet, 327, pp.307~310 (1986)
- T.K.K. Koo, "Feasibility of using EMG driven neuromusculoskeletal model fomovement of the elbow", J.Electromyo.Kinesiol., 15, pp.12~26 (2005)
- R.S. Barrett, "Individual muscle contributions to the swing phase of gait:An EMG-based forward dynamics modelling approach", Simul.Model.Pract.Th., 15, pp.1146~1155 (2007)
- Q. Shao, "A biomechanical model to estimate corrective changes in muscle activation patters for stroke patients", J.Biomech., 41, pp.3097~3100 (2008)
- Q. Shao, "An EMG-driven model to estimate muscle forces and jointmoments in stroke patients", Comput. Biol. Med, 39, pp.1083~1088 (2009)