More information can be obtained through:

Transcription

1 Example: multilevel Functional Orthopaedic Surgery Biomechanical models for the simulation of Orthopaedic surgery Carlo Frigo Department of Bioengineering Laboratory of Movement Biomechanics and Motor Control TBM Lab, Polytechnic of Milan, Milan, Italy Bache 2003 Pedotti A, Frigo C. EMG analysis of neuromuscular coordination in gait. In: Wiley Encyclopedia of Biomedical Engineering, Metin Akay, (ed), John Wiley & Sons, Inc. Hoboken (USA), Vol. 4, pages , (2006) DOI: / ebs1377 Joint and segments angles Gait analysis laboratory ne Ki at m ics Kinetics Motion analyser Force platforms Multi-channel Electromyograph Myo-ele ctric activity Joint moments and powers SEMG signals Different modes of getting information from movement analysis a) Just analysing kinematics, kinetics, and EMG signals b) Trying to analyse different variables jointly c) Developing models for further analysis d) Using additional or alternative movement analysis tools (inertial sensors, wearable devices not treated here) 1

2 More information can be obtained through: Direct Dynamics Problem (forward dynamics) Biomechanical model a) Dynamic system s modelling b) Musculoskeletal modelling External forces Joint moments Motion (kinematics) You need to know kinematics to compute forces and moments Inverse Dynamics Problem (inverse dynamics) Example of movement simulation (Direct dynamics problem) L F = K L F F = -K L 1 L F = K L 1 L All body segments move as a consequence of a single force applied (coupled dynamcis) F = -K L is the equivalente of λ in the Feldman equilibrium point theory (λ is the threshold of the Tonic Stretch Reflex) Pelvis fixed in space Foot fixed on the floor Dynamic models can be used to simulate movement perturbations and/or see the effects of changing the kinematics Data collection and processing system Thigh Biomechanical model Pelvis z y Translation reference xsystem Lumbo-sacral joint (3 d.o.f.) Shank Knee joint (2 d.o.f.) Ankle joint (2 d.o.f.) Foot Ground reaction (forces and moments) 2

3 Flexion-Extension axis Synthesis of human walking Pelvis Hip joint (neutral position) Femur Adduction-Abduction axis Internal-External rotation axis Hip joint (Flexion, Adduction, ExternalRotation) b) Musculoskeletal modelling Jo in t M o me n ts- N o kn e e yie ld in g Nm Aimed at surgery simulation and planning F/E -An k le R F/E -K n e e R F/E -H ip R # S a m p le s (t= 2 0 m s ) J o in t An g le s- N o k n e e yie ld in g Deg 40 A-An k le R 20 A-K n e e R A-H ip R -4 0 # S a m p l e s (t= 2 0 m s) From Scott Delp, 2001 Lifemod- BRG Musculographics- SIMM AnyBody Model AnyBody Group 3

4 Anthopometric data collection from bio-images and segmentation Checking for flexion-extension, adduction-abduction, internal external rotation and different joint movements Soft tissue modelling: muscles and ligaments How to use musculoskeletal models Adapt the model to individual anatomy Input the actual joint kinematics Input joint limitations (from clinical tests): tests): they correspond to limits of muscle lengthening Simulation of normal walking (virtual muscle length): length): we can see which muscles overcome their muscle lengthening limits Adapt the model to individual anatomy 4

5 Input the actual joint kinematics Example of Joint Analysis of EMG and Muscle Kinematics Analyse joint limitations Ex. Duncan/Ely test (Rectus Femoris): K H Ex. Max. Popliteal Angle (Hamstrings) H K Input joint limitations into the model Obtain the muscle length constraints S=S(θ k, θ H ) (θ K, θ H, f) = 0 Simulate normal (virtual) walking L=L(f) Simulate normal (virtual) walking Simulate normal (virtual) walking 5

6 Analysis of 9 CP children (crouched) All values in mm Rectus Femoris Semimembranosus Patient # Max L (real) Max L (virtual) Max L (test) Max L (real) Max L (virtual) Max L (test) Max L (real) Iliacus Max L (virtual) Max L (test) 1197XA 393,2 413, , , XA 524,1 550,3 577,9 431,1 444, XA 534,4 545,2 574,7 419,5 438,2 423, XA 527,8 519,1 553,6 365,1 410,6 370, XA 341, ,8 278,3 289,4 285, XA 567, ,3 478,6 472, XA ,4 410, ,25 308, XA 436,2 448, ,9 335,9 325, XA 470,9 488,9 502,3 359,4 381,2 370, AV 463,6 475,0 501,4 358,2 377,0 365,4 144,0 155,2 147,0 SD 54,9 53,5 46,1 30,7 43,2 35,9 7,2 17,1 18,8 Musculoskeletal modelling can help identifying short muscles and plan for proper intervention, but. Muscle models must be improved (wrapping surfaces, joint kinematics) Antropometric data should be better estimated on the individual subjects Muscular and tendineus components should be better identified Clinical test measurements must be included in the procedure Integration of bio-images into biomechanical model Sub-cutaneous tissue Muscle ultrasonography Motor control mechanisms and neural and muscular plasticity need to be better understood Fluoroscopy Contractile Connective tissue Aponeurosis tissue Lateral Gastrocnemius Advantages: Advantages: Soleus Kinematic images Good resolution Low radiation doses weakweak-points: points: Optimal identification of joint centres and axes of rotation Identification of Ligaments Image distorsion Limited field of view 6

7 The problem of Rotula Alta in CP children Surgical intervention Models can help understanding the biomechanical effects of surgery Thank You for Your attention 7