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**The Robotic Gait Simulator: A Dynamic Cadaveric Foot and Ankle Model for Biomechanics Research**

Patrick M. Aubin Department of Biomechanics,Vilnius Gediminas Technical University, Vilnius Lithuania Department of Electrical Engineering, University of Washington , Seattle, WA RR&D Center of Excellence, Department of Veterans Affairs, Seattle, WA

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**Motivation Cadaveric models Introduction RGS**

Olson SL, 2003, Muscular imbalances resulting in a clawed hallux. R. Bahr, 1998, Ligament force and joint motion in the intact ankle: a cadaveric study. Rembrandt, 1632 The Anatomy Lesson of Dr. Nicolaes Tulp RGS Cadaveric models Utility Fidelity

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**State of the Art Challenges for gait simulators**

Introduction State of the Art Medical School at Hannover, Germany Challenges for gait simulators control the vertical GRF scaled body weight tibia degrees of freedom speed U. of Salford and Iowa State U. Cleveland Clinic

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**General Problem Statement**

Introduction General Problem Statement Develop an RGS in vitro tibia kinematics, tendon forces, and ground reaction force (GRF) Use the RGS to evaluate novel biomedical devices (e.g. prosthetic feet) model normal and pathological gait evaluate surgical treatment strategies determine optimal surgical objectives elucidate disease etiology determine biological function

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**RGS Methods living subject in vivo gait trial R2000**

GRF tendon actuation muscle model tendon force plantar pressure cadaveric foot model foot & tibia kinematics EMG, PCSA from literature living subject kinematics

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**RGS R2000 parallel robot Force plate (C) Cadaveric foot (D)**

Methods RGS R2000 parallel robot Force plate (C) Cadaveric foot (D) Tibia mounting frame (F) Steel frame (A) Tendon actuation (G) 9 brushless DC motors Series load cells 3D motion tracking camera system (H)

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**The R2000 6-DOF 25 microns repeatability 120°/s yaw Methods**

© Mikrolar Inc. video

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**Iterative Learning Control**

Methods Iterative Learning Control Iteration domain vertical GRF control Simulation analyze vertical GRF adjust motion repeat R2000 tendon actuators prosthetic foot plantar surface tendons GRF target kinematics ground motion target GRF target tibia kinematics iterative learning controller

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ASB, Blacksburg, VA, 2006 NWBS, Seattle, WA, 2006

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**Prosthetic Gait Simulation**

Methods Prosthetic Gait Simulation Kinematics recorded from transtibial amputee video

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**Prosthetic Gait Simulation**

Results Prosthetic Gait Simulation Simulation results (1.5s) ILC: 6 iterations to vGRF tracking 4.1% BW RMS error: simulated vs. in situ P.M. Aubin, et al., IEEE Transactions on Biomedical Engineering, vol. 55, Mar. 2008

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**Manual vGRF Control Motivated to study the foot and ankle**

Motivation Manual vGRF Control Motivated to study the foot and ankle Improvements for cadaveric simulation Tendon force actuation Tibia mounting frame Liquid nitrogen freeze clamps Collaboration with Lyle Jackson UW medical student research training program

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**Tendon Force Actuation**

Methods Tendon Force Actuation Nine motors + load cells + freeze clamp Force feedback PID control Matlab Simulink model A/D tendon force torque command target force Gc(z) + - ZOH 1 current saturation PID drive actuator tendon system D/A load cell G(s)

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**Manual vGRF Control Manual control block diagram Methods R2000 tendon**

actuators target tendon force cadaveric foot plantar surface tendons GRF target kinematics ground motion tendon force target GRF target tibia kinematics manual control

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**Manual vGRF Control Control heuristics 0-40% of stance phase**

Methods Manual vGRF Control Control heuristics 0-40% of stance phase vGRF achieved by translating the mobile platform 50-90% of stance phase vGRF achieved by adjusting the Achilles tendon force

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Results Manual vGRF Control In vitro vertical GRF matched in vivo data

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ICRB, Banff, Canada, 2008. NACOB, Ann Arbor, MI, 2008. WSMRF, Carmel, CA, 2008.

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**Flatfoot Simulation Motivation Methods**

Introduction Flatfoot Simulation Motivation flatfoot incidence ~5%, (Ferciot, 1972) investigate effectiveness of reconstructive surgeries Methods manual vGRF control target tibial kinematics and GRF recorded from 10 flat foot subjects cadaveric flat foot ligament attenuation 15,000 cycles

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Results Flatfoot Simulation In vitro tibia angles matched in vivo data

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Results Flatfoot Simulation Collapse of the medial arch

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**Open Loop vGRF Control Manual vGRF control was non-dynamic**

Introduction Open Loop vGRF Control Manual vGRF control was non-dynamic poorly approximates a dynamic system Improvements for dynamic simulation faster tendon force actuator rise and settling time synchronization RGS software data analysis, left and right foot, dynamic tendon force trajectory path planning

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**FAchilles = G·PCSA·MST·EMG**

Methods Open Loop vGRF Control ∆x ROB vGRF Heuristics FAchilles = G·PCSA·MST·EMG R2000 tendon actuators target tendon force cadaveric foot plantar surface tendons GRF target kinematics ground motion tendon force target GRF target tibia kinematics ∆x G RGS operator

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Results Open Loop vGRF Control vertical GRF video

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**Open Loop vGRF Control Results vertical GRF in vivo Force (N/ ½ BW)**

stance phase (%) Force (N/ ½ BW) 100 in vivo in vitro vertical GRF 1

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ORS, Las Vegas, NV 2009 NWBS, Pullman, WA 2009

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**Arthrodesis of the First MTPJ**

Introduction Arthrodesis of the First MTPJ Metatarsalphalangeal joint (MTPJ) arthrodesis simulations Arthrodesis indications osteoarthritis previous failed surgeries Ahmad Bayomy Collaborator UW medical student research training program MTPJ Modified from

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**Arthrodesis of the First MTPJ**

Introduction Arthrodesis of the First MTPJ Literature suggests 20° to 25° of dorsiflexion Above 25°: Shoe wear difficulty Below 20°: Abnormal hallux pressure Dorsal fixation plate to simulate arthodesis Vary DF measure PP

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**Arthrodesis of the First MTPJ**

Methods Arthrodesis of the First MTPJ RGS simulation at ½ body weight and 10 s video

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**Arthrodesis of the First MTPJ**

Methods Arthrodesis of the First MTPJ The fusion angle that minimizes peak pressure under the hallux and first metatarsal was 24.0°.

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**Fuzzy logic 1.0 vGRF Control**

Introduction Fuzzy logic 1.0 vGRF Control Motivation vGRF fidelity stance phase (%) Force (N/ ½ BW) 100 in vivo in vitro manual control results

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**Fuzzy logic 1.0 vGRF Control**

Introduction Fuzzy logic 1.0 vGRF Control A fuzzy logic controller can addresses four major challenges: non-linear, time variant: heel strike (contact events), material properties ill-defined: knowledge is qualitative and descriptive, not analytical underdetermined: vGRF= f (nine tendons, tibia kinematics) limited number of simulations allowed neural networks and genetic algorithms not appropriate As a model-free paradigm a fuzzy rule based controller is well suited for highly nonlinear MIMO systems, [Ross, 2004].

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**Fuzzy logic 1.0 vGRF Control**

Introduction Fuzzy logic 1.0 vGRF Control Fuzzy logic controller replaces RGS operator R2000 tendon actuators target tendon force cadaveric foot plantar surface tendons GRF target kinematics ground motion tendon force target GRF target tibia kinematics fuzzy logic controller RGS operator

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**Fuzzy logic 1.0 vGRF Control**

Methods Fuzzy logic 1.0 vGRF Control Defuzzification Composition Inference Fuzzification membership function rule table max center of gravity fuzzy logic vertical GRF controller large neg. … zero … large pos. ∆FAchilles output variables fuzzy sets early late stance percent stance input variables fuzzy sets negative zero positive vGRFerror ∑vGRFerror input variables fuzzy sets

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**Fuzzy logic 1.0 vGRF Control**

Methods Fuzzy logic 1.0 vGRF Control Defuzzification Composition Inference Fuzzification membership function rule table max center of gravity fuzzy logic vertical GRF controller if stance is late and vGRFerror is positive and ∑vGRFerror is positive then change in Achilles tendon force is large positive If…. then … rules min implication

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**Fuzzy logic 1.0 vGRF Control**

Methods Fuzzy logic 1.0 vGRF Control Defuzzification Composition Inference Fuzzification membership function rule table max center of gravity fuzzy logic vertical GRF controller Combine fuzzy output subsets +

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**Fuzzy logic 1.0 vGRF Control**

Methods Fuzzy logic 1.0 vGRF Control Defuzzification Composition Inference Fuzzification membership function rule table max center of gravity fuzzy logic vertical GRF controller Determine crisp output via center of gravity

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**Fuzzy logic 1.0 vGRF Control**

Methods Fuzzy logic 1.0 vGRF Control Fuzzy sets manually tuned RGS simulations using modified single axis prosthetic foot

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**Fuzzy logic 1.0 vGRF Control**

Results Fuzzy logic 1.0 vGRF Control vGRF tracking performance 1.7% BW RMS tracking error between % stance.

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ORS, New Orleans, LA, NV, 2009 NWBS, Pullman, WA , 2009 ASB, College State, PA, 2009

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**Long Second Metatarsal**

Introduction Long Second Metatarsal Crossover toe deformity second metatarsophalangeal joint (MTPJ) proposed etiology: long second metatarsal Hypothesis: second metatarsal length is positively correlated with increased plantar pressure Joel Weber Collaborator MSRTP MTPJ

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**Long Second Metatarsal**

Methods Long Second Metatarsal Surgically lengthen second metatarsal Measure plantar pressure Measure second metatarsal angle Repeated measures design (6 feet, 5 lengths) Achilles tendon force from in vivo measurement

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**Long Second Metatarsal**

Methods Long Second Metatarsal RGS simulation at ½ body weight and 10 s video

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**Long Second Metatarsal**

Results Long Second Metatarsal vGRF tracking results

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**Long Second Metatarsal**

Results Long Second Metatarsal Second met head peak pressure was significantly associated with an increase in second met length (p=0.0005)

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NWBS, Seattle, WA , 2010 ASB, Providence, RI, 2010 iFAB, Seattle, WA , 2010

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**Fuzzy Logic 2.0 vGRF Control**

Introduction Fuzzy Logic 2.0 vGRF Control Motivation improve vGRF fidelity fuzzy logic controller R2000 tendon actuators target tendon force cadaveric foot plantar surface tendons GRF target kinematics ground motion tendon force target GRF target tibia kinematics RGS operator

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**Fuzzy Logic 2.0 vGRF Control**

Methods Fuzzy Logic 2.0 vGRF Control Three inputs and three outputs Three controllers in parallel Heuristics based on stance phase events Achilles tibialis anterior R2000 fuzzy logic controller ∆FACH ∆FTA ∆x vGRFerror ∑vGRFerror percent stance

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**Fuzzy Logic 2.0 vGRF Control**

Methods Fuzzy Logic 2.0 vGRF Control Heel strike no fuzzy logic output Load response tibialis anterior R2000 trajectory Midstance Late stance Achilles Heuristics: if vGRFerror is positive and ∑vGRFerror is positive then ∆Achilles tendon force is large positive if vGRFerror is positive and ∑vGRFerror is positive then ∆x is large positive if vGRFerror is positive and ∑vGRFerror is positive then ∆ tibialis tendon force is large positive

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**Fuzzy Logic 2.0 vGRF Control**

Methods Fuzzy Logic 2.0 vGRF Control R2000 robot PID tendon force controller electric motor actuators cadaveric foot tendons plantar surface load cell DA AD fuzzy logic vGRF ∆FAch trajectory generator vGRFtarget + ∆xj FAch Ftendonx 7 in vivo tibial kinematics force plate vGRFactual Σ _ FTA ∆FTA

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**Fuzzy Logic 2.0 vGRF Control**

Methods Fuzzy Logic 2.0 vGRF Control Statistics methods in vitro versus in vivo Linear mixed effects regression vertical GRF Two-sample t-tests tibia angles ˟ ˟ ˟ min time

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**Fuzzy Logic 2.0 vGRF Control**

Methods Fuzzy Logic 2.0 vGRF Control six feet, three learning trials, one final trial 2.7 s ¾ BW video

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**Fuzzy Logic 2.0 vGRF Control**

Results Fuzzy Logic 2.0 vGRF Control mean RMS vGRF tracking error was 5.9% BW sig. diff. (p<.05) minimum (5.9%) vGRF int. (2.0%) ˟

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**Fuzzy Logic 2.0 vGRF Control**

Results Fuzzy Logic 2.0 vGRF Control No sig. diff. between in vivo and in vitro tibial kinematics (p<0.05)

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**Fuzzy Logic 2.0 vGRF Control**

Results Fuzzy Logic 2.0 vGRF Control Tendon force tracking 3.6 N RMS 30.6% peak 3.8 N RMS 5.0% peak in vivo estimate in vitro mean

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**Fuzzy Logic 2.0 vGRF Control**

Discussion Fuzzy Logic 2.0 vGRF Control Close loop fuzzy logic vGRF control improvement over open loop control Increased speed to 2.7s Accurate reproduction of tibial kinematics vGRF tendon forces

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NWBS, Seattle, WA , 2010 ASB, Providence, RI, 2010 iFAB, Seattle, WA , 2010

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**Bony Motion Bony motion useful to understand joint function**

Introduction Bony Motion Bony motion useful to understand joint function Non-invasive and invasive methods C. Nester et al., 2007 A. Leardini et al., 2006

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**Bony Motion Study objectives**

Introduction Bony Motion Study objectives Develop an anatomical multi-segment foot model Determine foot bony motion during the stance phase of gait

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**Bony Motion Six cadaveric feet RGS simulations in 2.7s at ¾ BW**

Methods Bony Motion Six cadaveric feet RGS simulations in 2.7s at ¾ BW Multi-segment anatomical foot model

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**Bony Motion Anatomical multi-segment foot model**

Methods Bony Motion Anatomical multi-segment foot model digitized virtual points bone pins and quad clusters

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Methods Bony Motion RGS simulation at ¾ body weight and 2.7 s video

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**Bony Motion Motion of 17 joints recorded**

Results Bony Motion Motion of 17 joints recorded Midfoot joints have substantial motion Range of motion: 23.2± 4.6 Range of motion: 12.2± 2.2

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**Bony Motion In vitro results consistent with invasive in vivo data**

Discussion Bony Motion In vitro results consistent with invasive in vivo data Results indicate limitations of simplified rigid body models Better understanding of midtarsal joint midfoot motion inter-metatarsal mobility

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**Conclusion Dynamic vGRF tracking performance open loop Fuzzy v 1.0**

Stance phase (%) Force (N/ ½ BW) 100 in vivo in vitro open loop Fuzzy v 1.0 Fuzzy v 2.0

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**Conclusion iterative learning manual open loop fuzzy logic 1.0**

speed vGRF control Clinical study 1.5 s prosthetic gait simulation iterative learning static flatfoot simulation manual 10 s arthrodesis of first MTPJ open loop 10 s long second metatarsal fuzzy logic 1.0 2.7 s foot bony motion fuzzy logic 2.0

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Acknowledgements Department of Veterans Affairs, Research Rehabilitation and Development Service grant numbers A2661C, A3923R, A6669R and A4843C.

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Special thanks to: Center of Excellence for Limb Loss Prevention and Prosthetic Engineering

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**References Ferciot CF. Clin Orthop 85:7–10, 1972.**

Kaz, AJ. Foot Ankle Int. 28: , 2007. Nester, CJ. J. of Biomechanics 40: 3412– Leardini, A. Gait & Posture 25: , 2007.

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Extra slides

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**Motivation Introduction Hypothesis Conclusion scientific method**

Gray's Anatomy of the Human Body The function of A is B. Condition A causes disease B (etiology). Treatment A has a better outcome than treatment B. Experiment Data (Results) Cadaveric Model Living subjects Computational

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**State of the Art Dynamic cadaveric gait simulators Introduction**

Pennsylvania State U. U. of Salford and Iowa State U. Medical School at Hannover, Germany U. of Wisconsin-Milwaukee and Mayo Clinic Cleveland Clinic Vertical GRF control: iterative control Tibia DOF 6 Speed 3.2 s Vertical GRF control: Force control Tibia DOF 3 Speed 60 s Vertical GRF control Trial and error Tibia DOF 3 Speed 2 s Vertical GRF control Trial and error Tibia DOF 3 Speed 12 s Vertical GRF control: Trial and error Tibia DOF 3 Speed 20 s

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**Open Loop vGRF Control Achilles tendon dictates vGRF Results Achilles**

1.0 Force (N/ ½ BW) 0.5

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**Fuzzy logic 1.0 vGRF Control**

Methods Fuzzy logic 1.0 vGRF Control RGS Block diagram with fuzzy logic controller R2000 PID tendon force controller electric motor actuators cadaveric foot tendons plantar surface load cell DA AD fuzzy logic vGRF ∆FAch trajectory generator vGRFtarget + ∆xj FAch Ftendonx 8 in vivo tibial kinematics force plate vGRFactual Σ _ operator

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**Long Second Metatarsal**

Results Long Second Metatarsal ↑ second met length ↑ PP and pressure time integral (PTI) under second met head (p=0.005, p<0.0001) ↓ PP and PTI under first met head (p=0.029, p=0.024) ↑ second toe transverse plane angle (p=0.003)

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**Fuzzy Logic 2.0 vGRF Control**

Methods Fuzzy Logic 2.0 vGRF Control Stance phase events foot flat 16.6% COP under met heads 43.5% heel rise 50% peak TA force ~18%

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**Fuzzy Logic 2.0 vGRF Control**

Methods Fuzzy Logic 2.0 vGRF Control R2000 trajectory optimization to increase speed Vicon Plate trajectory Inverse kinematic map Motor velocity Optimization Best TIB pose ROB

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**Fuzzy Logic 2.0 vGRF Control**

Results Fuzzy Logic 2.0 vGRF Control Within subject variability

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**Fuzzy Logic 2.0 vGRF Control**

Results Fuzzy Logic 2.0 vGRF Control Medial/lateral and anterior/posterior GRF similar to in vivo medial/lateral anterior/posterior

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**Fuzzy Logic 2.0 vGRF Control**

Results Fuzzy Logic 2.0 vGRF Control Precise tibial kinematics

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**Conclusion Gait simulator comparison open loop 100 12 3 50 2**

system vGRF control vGRF (%) speed (s) tibial DOF Pennsylvania State Univ. open loop 100 12 3 Univ. of Salford & Iowa State Univ. 50 2 Medical School of Hannover, Germany force control 60 Univ. of Wisconsin- Milwaukee & Mayo Clinic 40 20 Cleveland Clinic iterative control 3.2 6 VA RR&D fuzzy logic 75 2.7

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