تعیین نیرو در کاراندازهای عضله ‌تاندون مختلف با استفاده از شبکه‌ی عصبی

نوع مقاله : مقاله های پژوهشی

نویسندگان

1 استادیار، گروه مهندسی مکانیک، دانشکده‌ی مهندسی، دانشگاه بوعلی سینا، همدان

2 کارشناس ارشد، گروه مهندسی معدن، گروه مهندسی، دانشگاه بوعلی سینا همدان

چکیده

مقدمه: وابستگی نیروی عضلانی به عوامل متعدد، باعث ایجاد محدودیت‌هایی در ارایه‌ی یک رابطه‌ی ریاضی صریح به منظور پیش‌بینی نیروی عضلانی گشته است. به علت اهمیت این رابطه در مسایل مختلف اسکلت عضلانی و در زمینه‌ی آنالیز حرکت، ارایه‌ی راهکارهایی برای تخمین تئوری نیروی عضلانی امری ضروری است. هر چند در مطالعات مختلف، روابط گوناگونی برای این منظور مطرح شده است، اما پیچیدگی ارتباط میان نیروی ایجاد شده در عضله و عوامل مؤثر در آن، باعث شده است تا تلاش برای ارایه‌ی یک رابطه‌ی ریاضی جامع با بازده محاسباتی بالا با مشکلات زیادی روبه‌رو شود. بنابراین روابط ارایه شده یا جامعیت لازم برای کاربرد در کاراندازهای عضله تاندون مختلف را ندارند و یا به شدت غیر خطی و حجیم هستند و دارای بازده محاسباتی مناسبی نیستند.روش‌ها: در این پژوهش پس از مرور روابط ریاضی مختلف ارایه شده برای پیش‌بینی نیروی عضلانی، از یک شبکه‌ی عصبی برای تخمین نیرو در کاراندازهای عضله تاندون استفاده شده است.یافته‌ها: در این پژوهش یک مدل با بازده محاسباتی بالا و پارامترهای محدود ارایه گردید که می‌تواند برای برآورد نیروی ایجاد شده در کاراندازهای عضله تاندون در طول و سرعت‌های انقباض متفاوت مورد استفاده قرار گیرد.نتیجه‌گیری: به کارگیری شبکه‌ی عصبی مصنوعی ما را قادر به برآورد سریع‌تر و دقیق‌تر نیروی عضلانی در شرایط مختلف می‌سازد. در اصل، این روش باعث افزایش راندمان محاسباتی و یا به عبارتی کاهش زمان حل خواهد شد.

کلیدواژه‌ها

عنوان مقاله [English]

Utilizing an Artificial Neural Network for Musculotendon Actuator Force Estimation

نویسندگان [English]

  • Mohsen Sadeghimehr 1
  • Davood Naderi 1
  • Mesbaholreza Sharifi 2
1 Assistant Professor, Department of Mechanical Engineering, School of Engineering, Bu-Ali Sina University, Hamadan, Iran
2 Department of Mining Engineering, School of Engineering, Bu-Ali Sina University, Hamadan, Iran
چکیده [English]

Background: Since muscle force depends on various factors, presenting explicit equations for predicting it is difficult. Due to the importance of these equations in different musculoskeletal problems and analysis of motion, proposing solutions for estimating muscle force is indisputable. Although numerous relations have been suggested, the complexity of relations between the caused musculotendon force and the effective factors leads to a lot of problems in suggesting efficient comprehensive computational equations. Thus, the previous proposed relations either do not have universality for estimating muscle force, or are too nonlinear, massive and inefficient.Methods: In this study, after reviewing previous mathematical relations for predicting muscle force, an artificial neural network was implemented to predict force in musculotendon actuators.Findings: The present study provided an appropriate and computationally efficient mathematical model with limited parameters. The model can be used to determine the forces generated by musculotendon actuators in various lengths and contraction velocities. Conclusion: Utilizing a neural network lets us estimate muscle force quicker and more accurately. This could fundamentally increase the computational efficiency in different musculoskeletal problems.

کلیدواژه‌ها [English]

  • Artificial Neural Network
  • Mathematical modeling
  • Musculotendon actuator muscle model

مراجع

  1. Huxley H, Hanson J. Changes in the Cross-Striations of Muscle during Contraction and Stretch and their Structural Interpretation. Nature 1954; 173: 973-6.
  2. Hill AV. The heat of shortening and the dynamic constants of muscle. 1938 vol 126 no 843 136-19 1938; 126(843): 136-95.
  3. Siebert T, Rode CH, Herzog W, Till O, Blickhan R. Nonlinearities make a difference: comparison of two common Hill-type models with real muscle. Biological Cybernetics 2008; 98(2): 133-43.
  4. Abbott BC, Wilkie DR. The relation between velocity of shortening and the tension-length curve of skeletal muscle. The Journal of Physiology 1953; 120(1-2): 214-23.
  5. Edman KA, Elzinga G, Noble MI. Enhancement of mechanical performance by stretch during tetanic contractions of vertebrate skeletal muscle fibres. J Physiol 1978; 281(1): 139-55.
  6. Bigland B, Lippold OCJ. The relation between force, velocity and integrated electrical activity in human muscles. J Physiol 1954; 123(1): 214-24.
  7. Brown IE, Cheng EJ, Loeb GE. Measured and modeled properties of mammalian skeletal muscle. II. The effects of stimulus frequency on force-length and force-velocity relationships. J Muscle Res Cell Motil 1999; 20(7): 627-43.
  8. Brown IE, Loeb GE. Measured and modeled properties of mammalian skeletal muscle: III. the effects of stimulus frequency on stretch-induced force enhancement and shortening-induced force depression. J Muscle Res Cell Motil 2000; 21(1): 21-31.
  9. Brown IE, Loeb GE. Measured and modeled properties of mammalian skeletal muscle: IV. dynamics of activation and deactivation. J Muscle Res Cell Motil 2000; 21(1): 33-47.
  10. Lemos RR, Epstein M, Herzog W. Modeling of skeletal muscle: the influence of tendon and aponeuroses compliance on the force-length relationship. Med Biol Eng Comput 2008; 46(1): 23-32.
  11. Zajac FE. Muscle and tendon: properties, models, scaling, and application to biomechanics and motor control. Crit Rev Biomed Eng 1989; 17(4): 359-411.
  12. Woo SL, Johnson GA, Smith BA. Mathematical modeling of ligaments and tendons. J Biomech Eng 1993; 115(4B): 468-73.
  13. Marsh RL, Bennett AF. Thermal dependence of contractile properties of skeletal muscle from the lizard Sceloporus occidentalis with comments on methods for fitting and comparing force-velocity curves. J Exp Biol 1986; 126: 63-77.
  14. Brooks SV, Faulkner JA. Contractile properties of skeletal muscles from young, adult and aged mice. J Physiol 1988; 404: 71-82.
  15. Julian FJ. The effect of calcium on the force-velocity relation of briefly glycerinated frog muscle fibres. J Physiol 1971; 218(1): 117-45.
  16. Ferenczi MA, Goldman YE, Simmons RM. The dependence of force and shortening velocity on substrate concentration in skinned muscle fibres from Rana temporaria. J Physiol 1984; 350: 519-43.
  17. Brown IE, Loeb GE. Measured and modeled properties of mammalian skeletal muscle. I. The effects of post-activation potentiation on the time course and velocity dependencies of force production. J Muscle Res Cell Motil 1999; 20(5-6): 443-56.
  18. Stelzer M, Stryk OV. Efficient Forward Dynamics Simulation and Optimization of Human Body Dynamics. Hochschulstrasse: Simulation and Systems Optimization Group, Technische Universit¨at Darmstadt; 2006.
  19. Pennestri E, Stefanelli R, Valentini PP, Vita L. Virtual musculo-skeletal model for the biomechanical analysis of the upper limb. J Biomech 2007; 40(6): 1350-61.
  20. Song D, Raphael G, Lan N, Loeb GE. Computationally efficient models of neuromuscular recruitment and mechanics. J Neural Eng 2008; 5(2): 175-84.
  21. Baratta RV, Solomonow M, Best R, D'Ambrosia R. Isotonic length/force models of nine different skeletal muscles. Med Biol Eng Comput 1993; 31(5): 449-58.
  22. Garner BA, Pandy MG. Estimation of musculotendon properties in the human upper limb. Ann Biomed Eng 2003; 31(2): 207-20.
  23. Chang YW, Su FC, Wu HW, An KN. Optimum length of muscle contraction. Clin Biomech (Bristol, Avon ) 1999; 14(8): 537-42.
  24. Winby CR, Lloyd DG, Kirk TB. Evaluation of different analytical methods for subject-specific scaling of musculotendon parameters. J Biomech 2008; 41(8): 1682-8.
  25. Deleze JB. The mechanical properties of the semitendinosus muscle at lengths greater than its length in the body. J Physiol 1961; 158: 154-64.
  26. Meijer K. Muscle mechanics; the effect of stretch and shortening on skeletal muscle force (thesis). Enschede: University of Twente; 1998.
  27. Otten E. Optimal design of vertebrate and insect sarcomeres. J Morphol 1987; 191(1): 49-62.
  28. Fenn WO, Marsh BS. Muscular force at different speeds of shortening. J Physiol 1935; 85(3): 277-97.
  29. Katz B. The relation between force and speed in muscular contraction. J Physiol 1939; 96(1): 45-64.
  30. Lannergren J. The force—velocity relation of isolated twitch and slow muscle fibres of Xenopus laevis. J Physiol 1978; 283: 501-21.
  31. Scovil CY, Ronsky JL. Sensitivity of a Hill-based muscle model to perturbations in model parameters. J Biomech 2006; 39(11): 2055-63.
  32. Curtin A, Woledge C. Power output and force-velocity relationship of live fibers from white myotomal muscle of the Dogfish Scyliorhinus canicula. J exp Biol 1988; 140: 187-97.
  33. Johnston IA, Salamonski J. Power output and force-velocity relationship of red and white muscle fibres from the Pacific blue marlin (Makaira nigricans). J Exp Biol 1984; 111: 171-7.
  34. Wilkie DR. The relation between force and velocity in human muscle. J Physiol 1949; 110(3-4): 249-80.
  35. Ritchie JM. The relation between force and velocity of shortening in rat muscle. J Physiol 1954; 123(3): 633-9.
  36. Daniels M, Noble MI, ter Keurs HE, Wohlfart B. Velocity of sarcomere shortening in rat cardiac muscle: relationship to force, sarcomere length, calcium and time. J Physiol 1984; 355: 367-81.
  37. Joyce GC, Rack PMH, Westbury DR. The mechanical properties of cat soleus muscle during controlled lengthening and shortening movements. J Physiol 1969; 204(2): 461-74.
  38. Edman KAP, Hwang JC. The force-velocity relationship in vertebrate muscle fibres at varied tonicity of the extracellular medium. The Journal of Physiology 1977; 269: 255-72.
  39. Cecchi G, Colomo F, Lombardi V. Force-velocity relation in normal and nitrate-treated frog single muscle fibres during rise of tension in an isometric tetanus. J Physiol 1978; 285: 257-73.
  40. Edman KA. The velocity of unloaded shortening and its relation to sarcomere length and isometric force in vertebrate muscle fibres. J Physiol 1979; 291: 143-59.
  41. Edman KA. Double-hyperbolic force-velocity relation in frog muscle fibres. J Physiol 1988; 404: 301-21.
  42. Edman KA, Mansson A, Caputo C. The biphasic force-velocity relationship in frog muscle fibres and its evaluation in terms of cross-bridge function. J Physiol 1997; 503: 141-56.
  43. Curtin NA, Edman A. Force-velocity relation for frog muscle fibres: effects of moderate fatigue and of intracellular acidification. J Physiol 1994; 475(3): 483-94.
  44. Morel JE. Force-velocity relationship in single muscle fibres. Journal of Theoretical Biology 1978; 73(3): 445-51.
  45. Askew GN, Marsh RL. Optimal shortening velocity (V/Vmax) of skeletal muscle during cyclical contractions: length-force effects and velocity-dependent activation and deactivation. J Exp Biol 1998; 201: 1527-40.
  46. Kasabov NK. Foundations of Neural Networks, Fuzzy Systems, andKnowledge Engineering. London: The MIT Press; 1998.
  47. Kamruzzaman J, Begg R, Sarker R. Artificial Neural Networks in Finance and Manufacturing. London: Idea Group Publishing; 2006.

فایل ها

سابقه مقاله

  • تاریخ دریافت: 03 مرداد 1390
  • تاریخ بازنگری: 11 آبان 1403
  • تاریخ پذیرش: 17 فروردین 1401

هم رسانی

ارجاع به این مقاله

آمار