Human Motion Control Course (Wb 2407)

Similar documents
Structure of Biological Materials

Modelling Muscle Contraction a multiscale approach

Lecture 13, 05 October 2004 Chapter 10, Muscle. Vertebrate Physiology ECOL 437 University of Arizona Fall instr: Kevin Bonine t.a.

Our patient for the day...

PHYSIOLOGY CHAPTER 9 MUSCLE TISSUE Fall 2016

According to the diagram, which of the following is NOT true?

EMG-Based Neuromuscular Modeling with Full Physiological Dynamics and Its Comparison with Modified Hill Model

Muscle tissue. Types. Functions. Cardiac, Smooth, and Skeletal

Acto-myosin: from muscles to single molecules. Justin Molloy MRC National Institute for Medical Research LONDON

Membrane Potential. 1. Resting membrane potential (RMP): 2. Action Potential (AP):

NOTE: LOOK ON MY WEBSITE FOR THE MUSCLE LABELING POWER POINT/PDF Part I. Identify the parts of the neuron that are labeled below.

Theeffectofmusclefatigueonthebehaviorofsinglemusclefibre

(Be sure to clearly state the principles addressed in your discussion.)

6I2.74I.63:6I2.8I7. averaging in one table only 65 p.c. and being rather variable. The

Some Properties of Linear Relaxation in Unfused Tetanus of Human Muscle

Cellular Electrophysiology and Biophysics

Modeling. EC-Coupling and Contraction

University of Warwick institutional repository: A Thesis Submitted for the Degree of PhD at the University of Warwick

BIOP2203 Biomechanics S1. BIOPHYSICS 2203: Biomechanics

Lectures 3, 9, 10, 11: Prenatal and

UNIT 6 THE MUSCULAR SYSTEM

ENBE 415 Example Problems Dr. Arthur T. Johnson. Example Calculate the expected times for men swimming 500 and 600 m in competition.

Navigation (Chapter 16)

Lecture 13, 04 Oct 2005 Chapter 16 & 17 Navigation & Muscle Function

the axons of the nerve meet with the muscle cell.

Practical Applications of Muscle Stimulation

Armin Toffler, The whole is more than the sum of the parts. Aristotle, BC. Prigogine I. and Stengers I. Order out of Chaos (1984)

The molecular basis of cardiac mechanics: Regulation of motor unit recruitment.

Sensory-Motor Neuroscience, Department of Physiology, Queen's University, Kingston, ON, Canada, K7L 3N6

Modeling. EC-Coupling and Contraction

Introduction to Physiology II: Control of Cell Volume and Membrane Potential

significant quantities are also thought to be produced by ATP splitting by the (Received 4 May 1984) length decreases below 2-20,um.

Some sensory receptors are specialized neurons while others are specialized cells that regulate neurons Figure 50.4

The Molecules of Movement Musc 1 - Professor Michael Ferenczi

BIOMECHANICS AND MOTOR CONTROL OF HUMAN MOVEMENT

Application of Newton/GMRES Method to Nonlinear Model Predictive Control of Functional Electrical Stimulation

A Model Based Analysis of Steady-State versus Dynamic Elements in the Relationship between Calcium and Force

LINEAR KINETICS (PART 2): WORK, ENERGY, AND POWER Readings: McGinnis Chapter 4

Membrane Protein Pumps

thebiotutor.com A2 Biology Unit 5 Responses, Nervous System & Muscles

Movement & Muscle. 19 th Lecture Fri 27 Feb Chapter 18. Vertebrate Physiology ECOL 437 (MCB/VetSci 437) Univ. of Arizona, spring 2009

Movement & Muscle Chapter 18

TISSUE. A) Types. (i)

BIOMECHANICS 3 Origins and consequences of forces in biological systems

Computational Modeling of the Cardiovascular and Neuronal System

1. True or false: at this moment, some of the muscle fibers in your gluteus maximus (a whole muscle) are contracting. a. True b.

OPTIMUM TAKE-OFF TECHNIQUES AND MUSCLE DESIGN FOR LONG JUMP

From sarcomeres to organisms: the role of muscle-tendon architecture in determining locomotor performance

Chapter 16. Cellular Movement: Motility and Contractility. Lectures by Kathleen Fitzpatrick Simon Fraser University Pearson Education, Inc.

The Biomechanics Behind Kicking a Soccer Ball

(Received 16 July 1965)

Muscles and Muscle Tissue: Part A

Photosynthesis and Cellular Respiration

Cardiac mechanoenergetics replicated by cross-bridge model Vendelin, M.; Bovendeerd, P.H.M.; Arts, M.G.J.; Engelbrecht, J.; van Campen, D.H.

Fundamentals of Neurosciences. Smooth Muscle. Dr. Kumar Sambamurti 613-SEI; ;

Mechanics of feline soleus: I1 Design and validation of a mathematical model

Neurophysiology. Danil Hammoudi.MD

A Cross-Bridge Model Describing the Mechanoenergetics of Actomyosin Interaction

STEIN IN-TERM EXAM -- BIOLOGY FEBRUARY 12, PAGE 1 of 7

Biology September 2015 Exam One FORM G KEY

Biology September 2015 Exam One FORM W KEY

Kinetics and Muscle Modeling of a Single Degree of Freedom Joint Part I: Mechanics

A novel electrical model of nerve and muscle using Pspice

Sarcomere Lattice Geometry Influences Cooperative Myosin Binding in Muscle

Cellular Energy: Respiration. Goals: Anaerobic respiration

Modeling and Simulation in Medicine and the Life Sciences

used as stubstituents on Ci and C2; namely H, CH 3, C 2 H 5, and CeHs. They are differentiated to have approximately

physical interaction enhance performance minimizing the risk

Passive Component. L m. B m. F pe. L w L t. Contractile. Active. M m. Component. L tm

Aerobic Cellular Respiration

Cross-bridge cycling theories cannot explain high-speed

Optimization of Active Muscle Force-Length Models Using Least Squares Curve Fitting

CIE Biology A-level Topic 15: Control and coordination

Modelling of gastrocnemius muscle using Hill s equation in COMSOL Multiphysics 4.0a

Department of Architecture & Civil Engineering

Effect of off-axis cell orientation on mechanical properties in smooth muscle tissue

Skeletal Muscle Tissue

Chapter 5. Table of Contents. Section 1 Energy and Living Things. Section 2 Photosynthesis. Section 3 Cellular Respiration

Identification of Joint Impedance

Research Article Spreading out Muscle Mass within a Hill-Type Model: A Computer Simulation Study

Power and Efficiency. Energy

BIOL Anatomy and Physiology I ( version L )

Compliant Realignment of Binding Sites in Muscle: Transient Behavior and Mechanical Tuning

SUPPLEMENTARY FIGURE 1. Force dependence of the unbinding rate: (a) Force-dependence

2. In regards to the fluid mosaic model, which of the following is TRUE?

Mitochondrial Biogenesis is the process by which new mitochondria are formed in the cell.

Form and Function. Physical Laws and Form. Chapter 40: Basic Principles of Animal Form and Function. AP Biology Fig Figs & 40.

Chem Lecture 9 Pumps and Channels Part 1

Kirchhoff s Rules. Survey available this week. $ closed loop. Quiz on a simple DC circuit. Quiz on a simple DC circuit

PROPERTY OF ELSEVIER SAMPLE CONTENT - NOT FINAL. The Nervous System and Muscle

Advanced Higher Biology. Unit 1- Cells and Proteins 2c) Membrane Proteins

Nonlinear Identification Method Corresponding to Muscle Property Variation in FES - Experiments in Paraplegic Patients

Slide 1. Slide 2. Slide 3. Muscles general information. Muscles - introduction. Microtubule Function

WAVE PROPAGATION ALONG FLAGELLA

The Proton Motive Force. Overview. Compartmentalization 11/6/2015. Chapter 21 Stryer Short Course. ATP synthesis Shuttles

ANIMAL MOVEMENT, MECHANICAL TUNING AND COUPLED SYSTEMS

Energy for biological processes

Biomechanical Modelling of Musculoskeletal Systems

activity it displays and the isometric tension it can exert. It has been shown

CELL BIOLOGY - CLUTCH CH. 9 - TRANSPORT ACROSS MEMBRANES.

Transcription:

Part 1 Human Motion Control Course (Wb 2407) Lecture 4 Muscles physiology, morphology and models Muscle morphology and physiology Morphology: fiber arrangement force-velocity relation force-length relation sarcomeres and fibers motor units Physiology: chemical energy to mechanical energy activation dynamics: calcium release and take-up contraction dynamics: cross-bridge formation muscle energy Shoulder muscles Leg muscles Muscle morphology Muscle morphology muscle belly + tendon or bony attachment parallel fibered - pennate (uni-, bi-, multi-) single - or mutiple headed single - or multiple bellied uni-, bi-, or multi articular 1

Force producing units: Sarcomeres Structure that actively generates the force cross-bridges release of cross-bridge: ATPase contraction mechanism action potential from nerve system transport along fiber (1-5 m/s) and into fiber triggering sarcoplasmatic reticulum (0.5-3 ms), release CA2+ detachment cross-bridge constant pump of CA2+ back in sarcoplasmatic reticulum attachment cross-bridge Contraction process: cross-bridge binding cross-bridges release of cross-bridge: Energy required (ATPase) ATP ADP+P +energy (with CA2+ as catalysor) 2

Muscle activation Transfer of information: from nerve pulse to muscle force Activation dynamics contraction dynamics nerve pulse Neuromuscular junction Action Potential (EMG) Calcium Release/uptake Active State: [Ca 2+ ] Crossbridge binding muscle force Transfer of information: from nerve pulse to muscle force Transfer of information: from nerve pulse to muscle force EMG Activation and contraction dynamics a( EMG Activation and contraction dynamics a( Muscle characteristics (F max, ƒ (length), ƒ (velocity) contractile element contractile element metabolites Metabolic history and fatigue neural excitation u( calcium transport [Ca] Cross bridge binding a( Muscle force fiber types one twitch tetanus: repeating twitches Type I: slow twitch, red, oxidative (aerobe), fatigue resistant Type IIa: fast twitch, red/pink, oxidative and glycolytic, fatigue resistant Type IIb: fast twitch, white, glycolytic (anaerobe), not fatigue resistant motor units: combination of fibers size principle force production: number of motor units or frequency 3

Consequences of morphology Influence of architecture: fiber length Effect of pennation (equal fiber length and volume) Larger force Lower speed (but higher initial speed!) Shorter trajectory Muscle structure Force-length relationship Force-velocity relationship Power-velocity relationship Peak Power Hill type models Myofascial force transmission F PE F mus F mus F CE F SE 1. Based on empirical relations 2. Input-output mapping of well defined experiments Musculoskeletal model often include muscles as separate actuators, Force from attachment to attachment but muscles do transfer force across their fascia s To other fibers To neighbouring agonists To nerves and vessels Even to antagonists Effects are not large, but might be important in low-force, or large displacement conditions Hill type model: experimental basis (1) Hill type model: experimental basis (1) A.V. Hill and O Meyerhof 4

Hill s equation for tetanized muscle Empirical relation for the force-velocity curve (A.V. Hill, 1938) Based on thermo-dynamics!! Nobel Prize 1922 V 0 (= bf 0 /a) ( v + b)( F + a) = b( F0 + a) Hill s equation for tetanized muscle Energy Balance: E=A+S+W E Energy release A Maintenance (activation) heat S Shortening heat W Work done v F F 0 a,b contraction velocity muscle force isometric tetanic force (constan constants F 0 isometric condition: quick release experiment, extra energy : Work done empirically: E = A S+W = b(f 0 -F) W = F.v S = a.v Hill s equation for tetanized muscle Force-length relationship Alternative formulations of force velocity relationship force generating element + non-linear damper F0 + a F = F0 v v + b = F0 k( v) v dimensionless force-velocity relation g(v), usually assumed independent of F 0 What is F 0? 1 ( v / v0) F = F0 = F0 g( v) 1+ c( v / v0) c = F0 / a ; v0 = b c F i = F f ( ) 0 l F o : isometric tetanic force at optimum length f(l) : dimensionless force-length relationship Force Length relationship Optimum fiber length 2,6 µm Maximal force range ~±50% Sarcomere force F = F q( f ( l) g( ) 0 v F i = F0 f ( l) q(: dimensionless activation dynamics (active state ~ [Ca 2 +]) f(l): force length relationship g(v): force velocity relationship F 0 : maximal isometric force Sarcomere force + muscle morphology muscle force 5

Force-length relation whole muscle Main energy consuming processes Calcium re-uptake in sarcoplasmatic reticulum: Related to active state Cross-bridge unbinding: Related to number of bound cross-bridges Energy from glucose breakdown Glycogen turn-over Glycogen + 3 ATP Pyruvate + 36 ATP Lactate An-aerobic 4 CO 2 + 12 H 2 O Aerobic 1 liter O 2 provides 21 kj energy! Usage anaerobic energy Oxygen consumption 1 liter oxygen provides 21 kj energy 80% heat 20% external power (mechanical energy) VO 2 -max: Maximal oxygen consumption (liters/minute) VO 2 -max men > VO 2 -max women decrease VO 2 -max with age increase VO 2 -max by training range VO 2 -max: 1.2-7.0 liters/minute 6

External power Moment x angular velocity External force x velocity Consumed power VO 2 : liters O 2 /minute 1 liter O 2 21 kj Efficiency external power Efficiency = consumed power Contents Hill-type models Hill (1938) Winters and Stark (1985) Cross-bridge models Huxley (1957) Zahalak (1981) Finite element models Part 2 Muscle models Cycling, walking: E 20-25% Manual wheelchair propulsion: E 8-10% Planimetric models Planimetric models simplified geometry 2-D constant muscle volume (Swammerdam, 1767) alternatives: model Otten F m = F f cos(α + β) cosβ revised model F m = F f sin(α + β) sinβ Hill-type models Hill-type models Original model (Hill, 1938): muscle as a large sarcomere with additional passive properties Muscle length Muscle velocity Neural input Activation dynamics q( contraction dynamics f(l), g(v) F max force F = F q( f ( l) g( ) 0 v 7

Hill-type models Hill-type models Many alternatives: Hatze (1981) Winters and Stark (1985) Zajac (1989) F mus = F SE cosα + F PE + F PV F SE = F CE F CE = F max q( f (l) g(v) Activation dynamics F CE = F q( f ( l) g( ) 0 v 1. q single parameter, 0<q<1 or 2. q defined by two 1 st order differential equations related to Neural activation (N a ) and calcium dynamics (ψ) dn τ a 1 + N a = u( ; dt dψ τ 2 dt +ψ = N ; a q( = q(ψ) Muscle dynamics Contraction dynamics u( N a = f1(na,u) ; ψ = f2( ψ,na ) ; l CE = f3(q( ψ ),lce,fse ) excitation activation 1 N a 1 ψ 1+ τ1 s 1+τ 2s q( q (ψ) f ( l) g( v) F max F PE l, v F( Input: active state q(, muscle length l mus State variable: length CE, l ce Length SE: l se = l mus l ce Output: F ce = F se = f 1 (l se ) Derivative state variable: contraction velocity v ce = f(l ce, q() [Ca 2+ ] Active state Hypothetical neural input EMG Calcium dynamics: activation: τ 2 = 10 msec de-activation: τ 2 = 50 msec Bandwidth Neural system τ 1 = 25 msec force length force - velocity F se = q( * f(l ce ) * g(v ce ) * F max g(v ce ) = q( * f(l ce ) * F max / F se (= F rel ) Inverse force velocity relation v ce = g -1 (F rel ) Hill-type models remarks Are active state, force-length and force-velocity independent? Large number of (unknown) parameters for each human muscle (>100), data based on animal experiments Parameters constant? No link to microscopic mechanism Negative stiffness in force-length characteristic, but Force-velocity relation increases impedance Cross-bridge models (Huxley, 1957) Nobel Prize 1963!! attachment and detachment rate functions f and g describe probabilities describes fraction of attached cross-bridges n( per length x 8

Cross-bridge models attachment and detachment rate functions f and g describe probabilities describes fraction of attached cross-bridges n( per length x k is linear cross-bridge stiffness F x = k.x is cross-bridge force Muscle force F depends on distribution n( of cross-bridges dn( n n = + v = f ( x){ N n( } g( x) n( dt t x F = k x n( dx n( F(x) Cross-bridge models k is linear cross-bridge stiffness F x = k.x is cross-bridge force Muscle force F depends on distribution n( of cross-bridges Muscle stiffness K depends on cross-bridge stiffness k dn( n n = + v = f ( x){ N n( } g( x) n( dt t x F = k x n( dx df K = = k n( dx dx n( F(x) Cross-bridge models (Zahalak, 1981) Cross-bridge distribution (Zahalak, 1981) Rewrite distribution equation in set of ordinary differential equations with distribution moments Q λ λ Qλ ( = x n( dx λ = 0,1,2,... Q ( t ) = h ( t ) + v Q 1( t λ λ λ ) Assume a normal distribution (Gaussian curve) for n( Huxley-model Zahalak-model Cross-bridge models Summary Hill-type models remarks rather complex shape of functions f and g is arbitrary to a certain extent explains force-velocity curve activation dynamics that has to be added is more complex possible to predict chemical energy good prediction of force and stiffness length velocity activation Hill-model force 3 inputs, 1 output Reasonable good for activation input Not good for length input (~ stiffness) derivative force-length relationship Underestimation muscle stiffness Not good for velocity input (~ viscosity) Derivative force-velocity relationship Overestimation muscle viscosity 9

Combined Hill-Huxley-type models Summary Huxley-type models length velocity activation Hill- Huxleymodel force length velocity activation Hill-model force Force-velocity curve from cross-bridge model Force-length & activation dynamics from Hill model Does not exist yet! 1 inputs, 1 output No force-length relationship, no activation dynamics Finite element models Muscle power Combining shape and function Hill type CE in user defined elements large deformations application: aponeurotomy experiment strain Active stress 10