The Influence of Posture, Applied Force and Perturbation Direction on Hip Joint Viscoelasticity

Huang, Hsien-Yung; Arami, Arash; Farkhatdinov, Ildar; Formica, Domenico; Burdet, Etienne

doi:10.1109/tnsre.2020.2983515

Cited by 13 publications

(18 citation statements)

References 27 publications

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“…The human central nervous system modulates muscle activation to generate the required torque and variable mechanical impedance at our joints to move and stabilize our motion [43]. Despite existing efforts in identifying mechanical joint impedance [44][45][46][47], multi-joint mechanical impedance have not yet fully uncovered during motion. In order to improve our simulations, the variable impedance can be also implemented in the future.…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

2021

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In this paper, we present a novel adaptation rule to optimize the exoskeleton assistance in rehabilitation tasks. The proposed method adapts the exoskeleton contribution to user impairment severity without any prior knowledge about the user motor capacity. The proposed controller is a combination of an adaptive feedforward controller and a low gain adaptive PD controller. The PD controller guarantees the stability of the human-exoskeleton system during feedforward torque adaptation by utilizing only the human-exoskeleton joint positions as the sensory feedback for assistive torque optimization. In addition to providing a convergence proof, in order to study the performance of our method we applied it to a simplified 2-DOF model of human-arm and a generic 9-DOF model of lower limb to perform walking. In each simulated task, we implemented the impaired human torque to be insufficient for the task completion. Moreover, the scenarios that violate our convergence proof assumptions are considered. The simulation results show a converging behavior for the proposed controller; the maximum convergence time of 20 s is observed. In addition, a stable control performance that optimally supplements the remaining user motor contribution is observed; the joint angle tracking error in steady condition and its improvement compared to the start of adaptation are as follows: shoulder 0.96±2.53° (76%); elbow −0.35±0.81° (33%); hip 0.10±0.86° (38%); knee −0.19±0.67° (25%); and ankle −0.05±0.20° (60%). The presented simulation results verify the robustness of proposed adaptive method in cases that differ from our mathematical assumptions and indicate its potentials to be used in practice.

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Section: Discussionmentioning

confidence: 99%

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

2021

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“…However, the authors did not include neural parameters in their torque estimation model, and instead used measured EMG to estimate the neuromuscular activity due to stretch reflex. Recent advancements in joint mechanical impedance estimation during active movements [ 91 , 92 , 93 , 94 ] would allow further investigations on how spasticity affects the modulation of joint impedance, particularly joint stiffness and viscosity, during volitional movement and walking.…”

Section: Discussionmentioning

confidence: 99%

Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation

Cha

Arami

2020

Sensors

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Spasticity, a common symptom in patients with upper motor neuron lesions, reduces the ability of a person to freely move their limbs by generating unwanted reflexes. Spasticity can interfere with rehabilitation programs and cause pain, muscle atrophy and musculoskeletal deformities. Despite its prevalence, it is not commonly understood. Widely used clinical scores are neither accurate nor reliable for spasticity assessment and follow up of treatments. Advancement of wearable sensors, signal processing and robotic platforms have enabled new developments and modeling approaches to better quantify spasticity. In this paper, we review quantitative modeling techniques that have been used for evaluating spasticity. These models generate objective measures to assess spasticity and use different approaches, such as purely mechanical modeling, musculoskeletal and neurological modeling, and threshold control-based modeling. We compare their advantages and limitations and discuss the recommendations for future studies. Finally, we discuss the focus on treatment and rehabilitation and the need for further investigation in those directions.

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“…standing or seated. These experiments showed that joint impedance depends on various factors, including joint angles, joint velocities and muscle activity [9,13,33,6,29,5,25,30]. The results imply that joint impedance must vary during movement.…”

Section: Introductionmentioning

confidence: 88%

“…Joint impedance is estimated by measuring the response to mechanical perturbations applied to the joint by robotic devices and is often expressed in terms of joint inertia, damping and stiffness [19,10,23,6,9,13,33,7]. Identification of hip, knee and ankle joint impedance has previously been performed under static conditions, e.g.…”

Section: Introductionmentioning

confidence: 99%

Identification of Hip and Knee Joint Impedance During the Swing Phase of Walking

van der Kooij,

Fricke,

Veld

et al. 2021

Preprint

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Knowledge on joint impedance during walking in various conditions is relevant for clinical decision making and the development of robotic gait trainers, leg prostheses, leg orthotics, and wearable exoskeletons. Whereas ankle impedance during walking has been experimentally assessed, knee and hip joint impedance during walking have not been identified yet. Here we developed and evaluated a lower limb perturbator to identify hip, knee and ankle joint impedance during treadmill walking. The lower limb perturbator (LOPER) consists of an actuator connected to the thigh via rods. The LOPER allows to apply force perturbations to a free-hanging leg, while standing on the contralateral leg, with a bandwidth of up to 39 Hz. While walking in minimal impedance mode, the interaction forces between LOPER and the thigh were low (<5 N) and the effect on the walking pattern was smaller than the within-subject variability during normal walking. Using a non-linear multibody dynamical model of swing leg dynamics, the hip, knee and ankle joint impedance were estimated at three time points during the swing phase for nine subjects walking at a speed of 0.5 m/s. The identified model was well able to predict the experimental responses, since the mean variance accounted for was 99%, 96%, and 77%, for the hip, knee and ankle respectively. The averaged across subjects stiffness varied between the three time point within 34-66 Nm/rad, 0-3.5 Nm/rad, and 2.5-24 Nm/rad for the hip, knee and ankle joint respectively. The damping varied between 1.9-4.6 Nms/rad, 0.02-0.14 Nms/rad, and 0.2-2.4 Nms/rad for hip, knee, and ankle respectively. The developed LOPER has a negligible effect on the unperturbed walking pattern and allows to identify hip, knee and ankle joint impedance during the swing phase.

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The Influence of Posture, Applied Force and Perturbation Direction on Hip Joint Viscoelasticity

Cited by 13 publications

References 27 publications

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

An Adaptive Assistance Controller to Optimize the Exoskeleton Contribution in Rehabilitation

Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation

Identification of Hip and Knee Joint Impedance During the Swing Phase of Walking

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