Identification of Joint Impedance tools for understanding the human - PowerPoint PPT Presentation

Identification of Joint Impedance tools for understanding the human motion system, treatment selection and evaluation Lecture 12 SIPE 2010 Case Studies Erwin de Vlugt, PhD Delft University of Technology

Delft-Leiden Research Connection Laboratory for Kinematics and Neuromechanics Hans Arendzen, Jurriaan de Groot, Carel Frans van der Helm, Erwin de Vlugt, Meskers, Frans Steenbrink, Erwin de Alfred Schouten, Herman van der Kooij, Vlugt, Asbjorn Klomp, Hanneke van der David Abbink, Riender Happee, Winfred Krogt, Andrea Maier, Bob van Hilten, Rob Mugge, Alistair Vardy, Judith Visser, Stijn Nelissen van Eesbeek Mission: application and validation of Mission: development of SIPE technology SIPE technology in the clinical practice to to analyze the human neuromuscular improve efficacy of intervention control system Delft University of Technology

Robots for System Identification • Mechanical energy transfer to the biological system • Measurement of forces and movement Delft University of Technology

Robots for System Identification - Natural tasks - Perturbations - Closed loop - Interpretable parameters Delft University of Technology

System Identification & Parameter Estimation (SI-PE) input output Physical System Identification minimal a priori System Behavior (SI) knowledge required Parameterization A priori knowledge Physical Properties required (PE) Delft University of Technology

Three case studies 1. Linear SIPE: intrinsic and reflexive properties of the shoulder (1DOF) 2. Linear SIPE: … but now for 3DOF (shoulder, elbow, wrist) 3. Nonlinear PE: intrinsic and reflexive torque of the ankle in stroke Delft University of Technology

Linear systems: Frequency domain analysis of mass-damper-spring 1 Stiffness Damping Mass = ( ) H s + + 2 Ms Bs K -3 = π 10 s 2 f Gain -4 10 • H is causal -1 0 1 • H is an admittance 10 10 10 Phase [deg] K ω = -50 0 M -100 -150 B β = -1 0 1 10 10 10 2 KM Frequency [Hz] Delft University of Technology

Optimal Admittance Control • Simulations indicate that contribution of reflexes decrease with frequency of torque input. Delft De Vlugt et al. 2001 University of Technology

Short Intro to Optimal Admittance Control Joint Admittance • is the dynamic relationship between joint angle and joint torque • the result of visco-elasticity and torque generated by reflexes • important for posture maintenance Research Questions • does admittance depend on the dynamic properties of external load, e.g. damping ? • how does admittance change with joint angle? Delft University of Technology

Case 1: 1DOF shoulder joint control Delft University of Technology

Recordings University of Technology Delft

Procedures • External damping B E : 0 – 400 Ns/m • External mass M E : 0.6 – 10 kg • Unpredictable force disturbances • 40 s (0.1-20 Hz) • Grip displacements ≈ 3 mm (SD) • EMG of four shoulder muscles • n= 5 (healthy) Delft University of Technology

Force perturbations: closed loop D ε F X X REF Σ Σ Human Environment - Delft University of Technology

Force perturbations: closed loop D 2 + - L m s Σ E Environment + b s k E E F X A X REF 1 1 Σ Σ Activation τ Intrinsic Arm + + s + s 1 2 m s b k - A Neural Muscle + − e s T k s k D v p Delay Spindles Human Arm Delft University of Technology

SI Results: Frequency Response Functions damper off damper on Delft University of Technology

Parameter Estimation (PE) FRF Estimation D Hold position F hand X hand Parameter Fit Linear Endpoint Model FRF Simulation Delft University of Technology

PE Results: stretch reflex estimates Delft De Vlugt et al. 2002 University of Technology

Results: optimized stretch reflex Delft University of Technology

Reflexive Admittance Control: no environment Delft University of Technology

Reflexive Admittance Control: with External Damper Delft University of Technology

Case 2: SIPE in the 3DOF Shoulder Delft University of Technology

data model 2DOF FRFs University of Technology Delft

PE Result: intrinsic parameters Delft De Vlugt et al. 2006 University of Technology

Stiffness Ellipses data model reflexes turned of / turned on Delft University of Technology

Case 3: Nonlinear case: Ramp-hold Ankle rotation in stroke Delft De Vlugt et al. 2010 University of Technology

Nonlinear case: Ramp-hold Ankle rotation • stroke (n = 19) Goal: • estimate passive visco-elasticity and stretch reflex dynamics and compare to Ashworth Scale Delft University of Technology

Direct Physical Parameterization Delft University of Technology

No Identification, Direct Parameterization direct parameterization of a nonlinear model in time domain Parameters: • inertia • tissue viscosity • tissue elasticity • activation dynamics • contractile dynamics Delft University of Technology

Main Result • Detailed parameterization possible: - Accurate (VAF > 90%) - Valid (low parameter SEM) • Viscosity decreased with movement velocity • Passive stiffness correlated to Ashworth Scale Delft University of Technology

Challenges: SIPE during movement • Time Varying Joint Admittance • Wavelets and subspace techniques • Collaboration between the fac. of 3ME (DCSC, BMechE) and Aerospace Eng. Delft University of Technology

Summary • Linear behavior: frequency domain can be used and provides direct qualitative information about the human joint dynamics. • Nonlinear behavior: time domain analysis by direct parameterization of a physical nonlinear model of the human joint. • Towards Time Varying System Identification…. Delft University of Technology

Graduate Student Master Projects • Master Projects at NMC Lab involves a mixture of SIPE, physiology and clinical issues • Many (international) opportunities for Graduate Students • internship (stage), preferably outside the Netherlands • fundamental projects: TUD • clinical projects: LUMC, Erasmus MC, VUMC Delft University of Technology