Human-exoskeleton control simulation, kinetic and kinematic modeling and parameters extraction

Khamar, Maryam; Edrisi, Mehdi; Zahiri, Mohsen

doi:10.1016/j.mex.2019.08.014

Cited by 24 publications

(13 citation statements)

References 5 publications

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“…Prior studies have developed virtual simulators of EAL in custom-built exoskeletons [44-47, 49, 64], but these devices are not FDA-approved and are likely many years away from widespread application for the rehabilitation of patients with neurological disorders. Prior studies have also developed virtual simulators of EAL based on idealized or imaginary exoskeletons [37, 40–42, 48, 50]. For example, Bianco et al developed a virtual simulator to examine how multi-joint assistance affects the metabolic cost of exoskeletal-assisted and unassisted walking [37].…”

Section: Discussionmentioning

confidence: 99%

Comparison of the dynamics of exoskeletal-assisted and unassisted locomotion in an FDA-approved lower extremity device: Controlled experiments and development of a subject-specific virtual simulator

Chandran

Nam

Hexner³

et al. 2022

Preprint

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Robotic exoskeletons have considerable, but largely untapped, potential to restore mobility in individuals with neurological disorders, and other conditions that result in partial or complete immobilization. The growing demand for these devices necessitates the development of technology to characterize the human-robot system during exoskeletal-assisted locomotion (EAL) and accelerate robot design refinements. The goal of this study was to combine controlled experiments with computational modeling to build a virtual simulator of EAL. The first objective was to acquire a minimum empirical dataset comprising human-robot kinematics, ground reaction forces, and electromyography during exoskeletal-assisted and unassisted locomotion from an able-bodied participant. The second objective was to quantify the dynamics of the human-robot system using a subject-specific virtual simulator reproducing EAL compared to the dynamics of normal gait. We trained an able-bodied participant to ambulate independently in a Food and Drug Administration-approved exoskeleton, the ReWalk P6.0 (ReWalk Robotics, Yoknaem, Israel). We analyzed the motion of the participant during exoskeletal-assisted and unassisted walking, sit-to-stand, and stand-to-sit maneuvers, with simultaneous measurements of (i) three-dimensional marker trajectories, (ii) ground reaction forces, (iii) electromyography, and (iv) exoskeleton encoder data. We created a virtual simulator in OpenSim, comprising a whole-body musculoskeletal model and a full-scale exoskeleton model, to determine the joint kinematics and moments during exoskeletal-assisted and unassisted maneuvers. Mean peak knee flexion angles of the human subject during exoskeletal-assisted walking were 50.1° ± 0.6° (left) and 52.6° ± 0.7° (right), compared to 68.6° ± 0.3° (left) and 70.7° ± 1.1° (right) during unassisted walking. Mean peak knee extension moments during exoskeletal-assisted walking were 0.10 ± 0.10 Nm/kg (left) and 0.22 ± 0.11 Nm/kg (right), compared to 0.64 ± 0.07 Nm/kg (left) and 0.73 ± 0.10 Nm/kg (right) during unassisted walking. This work provides a foundation for parametric studies to characterize the effects of human and robot design variables, and predictive modeling to optimize human-robot interaction during EAL.

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Section: Discussionmentioning

confidence: 99%

Comparison of the dynamics of exoskeletal-assisted and unassisted locomotion in an FDA-approved lower extremity device: Controlled experiments and development of a subject-specific virtual simulator

Chandran

Nam

Hexner³

et al. 2022

Preprint

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“…Previous work in gait simulations with biomechatronic devices has often relied on abstract representations of components such as motors, electronics, and control systems [16,37,38]. In contrast, Simscape Multibody and Simulink offer a large library of these components, which can be readily integrated into the model for more realistic representations of biomechatronic systems.…”

Section: Discussionmentioning

confidence: 99%

A Computational Gait Model With a Below-Knee Amputation and a Semi-Active Variable-Stiffness Foot Prosthesis

McGeehan

Adamczyk

Nichols

et al. 2021

Journal of Biomechanical Engineering

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Introduction: Simulations based on computational musculoskeletal models are powerful tools for evaluating effects of potential biomechanical interventions, such as implementing a novel prosthesis. However, the utility of simulations to evaluate effects of prosthesis design parameters on gait mechanics has not been fully realized due to lack of a readily-available limb loss-specific gait model and methods for efficiently modeling the energy storage and return dynamics of passive foot prostheses. The purpose of this study was to develop and validate a forward simulation-capable gait model with lower limb loss and a semi-active variable-stiffness foot (VSF) prosthesis. Methods: A seven-segment 28-DoF gait model was developed and forward kinematics simulations, in which experimentally-observed joint kinematics were applied and resulting foot contact forces evolved accordingly, were computed for four subjects with unilateral below-knee amputation walking with a VSF. Results: Model-predicted resultant ground reaction force (GRFR) matched well under trial-specific optimized parameter conditions (mean R2: 0.97, RMSE: 7.7% body weight (BW)) and unoptimized (subject-specific, not trial-specific) parameter conditions (mean R2: 0.93, RMSE: 12% BW). Simulated anterior-posterior center of pressure demonstrated mean R2 = 0.64 and RMSE = 14% foot length. Simulated kinematics remained consistent with input data (0.23 deg RMSE, R2 > 0.99) for all conditions. Conclusions: These methods may be useful for simulating gait of individuals with lower limb loss and predicting GRFR with novel VSF prostheses. Such data are useful to optimize user-specific prosthesis design parameters.

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“…Following equation describes the B-spline approximation for the joint angle profiles q i . q i t, p ðÞ ¼ X m j¼0 N j t ðÞ p j (11) where t ={t 1 , … ,t s } is knot vector, p ={p 1 , … ,p m } is the control points, N j is the basis function of B-spline function approximation. Here, control points P becomes optimization variables.…”

Section: Variablesmentioning

confidence: 99%

“…The modeling and simulation for human-exoskeleton is developed in [11]. In this study, we also use optimization-based motion prediction and simulation method with recursive Lagrange's equations of motion which is highly nonlinear.…”

Section: Introductionmentioning

confidence: 99%

Optimization Based Dynamic Human Motion Prediction with Modular Exoskeleton Robots as Interactive Forces: The Case of Weight Lifting Motion

Chung¹

2021

Collaborative and Humanoid Robots [Working Title]

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The optimization-based dynamics model is formulated for the weight lifting motion with human and exoskeleton model as interactive force term in this chapter. In the optimization algorithm, the human motion is defined as variables so that the motion which we want to generate (box lifting motion in this case) can be predicted. The objective function or cost function is defined as performance measure which can be switched by developer. In this paper we use the summation of each joint torque square which is considered as the dynamic effort for the motion. Constraints are defined as joint limits, torque limits, hand position, dynamic balance, exoskeleton assistive points, etc. Interaction force form exoskeleton robot can be derived as generalized coordinates and generalized force which are related to inertial reference frame and human body frame. The results can show how effective the exoskeleton robots are according to their assistive force.

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Human-exoskeleton control simulation, kinetic and kinematic modeling and parameters extraction

Abstract: Graphical abstract

Cited by 24 publications

References 5 publications

Comparison of the dynamics of exoskeletal-assisted and unassisted locomotion in an FDA-approved lower extremity device: Controlled experiments and development of a subject-specific virtual simulator

Comparison of the dynamics of exoskeletal-assisted and unassisted locomotion in an FDA-approved lower extremity device: Controlled experiments and development of a subject-specific virtual simulator

A Computational Gait Model With a Below-Knee Amputation and a Semi-Active Variable-Stiffness Foot Prosthesis

Optimization Based Dynamic Human Motion Prediction with Modular Exoskeleton Robots as Interactive Forces: The Case of Weight Lifting Motion

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