Unbiased Estimation of Human Joint Intrinsic Mechanical Properties During Movement

Guarín, Diego L.; Kearney, Robert E.

doi:10.1109/tnsre.2018.2870330

Cited by 12 publications

(16 citation statements)

References 40 publications

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“…www.nature.com/scientificreports www.nature.com/scientificreports/ Co-contraction is a strategy commonly used to increase joint stiffness and stability. However, it is important to understand that both intrinsic and reflex stiffness contribute to the overall stiffness at any joint 30 . As done in the current study, changes in reflex stiffness can be observed through changes in EMG 30 .…”

Section: Discussionmentioning

confidence: 99%

“…However, it is important to understand that both intrinsic and reflex stiffness contribute to the overall stiffness at any joint 30 . As done in the current study, changes in reflex stiffness can be observed through changes in EMG 30 . However, changes in intrinsic stiffness have not been quantified in this study and findings are therefore referring to alteration in reflexes as a contributor to stiffness.…”

Section: Discussionmentioning

confidence: 99%

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Investigating the Muscular and Kinematic Responses to Sudden Wrist Perturbations During a Dynamic Tracking Task

Forman

Avila-Mireles

et al. 2020

Sci Rep

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Sudden disturbances (perturbations) to the hand and wrist are commonplace in daily activities and workplaces when interacting with tools and the environment. it is important to understand how perturbations influence forearm musculature and task performance when identifying injury mechanisms. The purpose of this work was to evaluate changes in forearm muscle activity and co-contraction caused by wrist perturbations during a dynamic wrist tracking task. Surface electromyography was recorded from eight muscles of the upper-limb. Participants performed trials consisting of 17 repetitions of ±40° of wrist flexion/extension using a robotic device. During trials, participants received radial or ulnar perturbations that were delivered during flexion or extension, and with known or unknown timing. Co-contraction ratios for all muscle pairs showed significantly greater extensor activity across all experimental conditions. Of all antagonistic muscle pairs, the flexor carpi radialis (FCR)-extensor carpi radialis (ECR) muscle pair had the greatest change in co-contraction, producing 1602% greater co-contraction during flexion trials than during extensions trials. Expected perturbations produced greater anticipatory (immediately prior to the perturbation) muscle activity than unexpected, resulting in a 30% decrease in wrist displacement. While improving performance, this increase in anticipatory muscle activity may leave muscles susceptible to early-onset fatigue, which could lead to chronic overuse injuries in the workplace.www.nature.com/scientificreports www.nature.com/scientificreports/ co-contraction, the CNS has the ability to adapt and selectively alter joint impedance based on the magnitude and direction of instability 8 . Developing an internal model to learn optimal joint impedance is not always required to manage instability; Crevecoeur et al. (2019) explored a model-free strategy, reporting that encountering a single unexpected perturbation led to an immediate increase in co-contraction followed by a gradual decrease during subsequent, perturbation-free trials 9 . When a wrist perturbation is expected but timing is unknown, anticipatory co-contraction is significantly greater than when the timing is known 10 . These findings could be a reflection of the methodology, since participants knew that a perturbation trial would occur, but were unaware of the timing. At the wrist joint, this co-contraction is illustrated by the stabilizing nature of the wrist extensors, which remain active when performing static tasks, even when they are not the primary muscles involved [11][12][13] . This leads to an optimization problem for the CNS as control of impedance is computationally costly 8 , while co-contraction is metabolically costly 6 . Therefore, to optimize control, the CNS is required to provide adequate impedance relative to the magnitude and direction of instability while utilizing minimal metabolic fuel 8,9 . Research utilizing unexpected perturbations at the distal upper extremity has largely been focused around sta...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Investigating the Muscular and Kinematic Responses to Sudden Wrist Perturbations During a Dynamic Tracking Task

Forman

Avila-Mireles

et al. 2020

Sci Rep

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“…This paper does not intend to differentiate the contribution of reflexes from intrinsic properties of the human arm, like it was done in [21], but rather try to observe the global behaviour of the arm. To that purpose, the simplified KBI model ( 3) for the human arm behaviour is proposed at the endpoint level, without coupling between the different axes, that is with diagonal parameter matrices.…”

Section: B Human Impedance In a Rhythmic Taskmentioning

confidence: 99%

“…These models do not intended to explain the complete human motor control, but rather reproduce some of its aspects. More complete models [10], [21] are required to understand the human CNS.…”

Section: Introductionmentioning

confidence: 99%

Interactive robotics for human impedance estimation in a rhythmic task

Fortineau

Makarov

Rodríguez-Ayerbe

et al. 2020

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

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This paper presents an experimental robotic setup dedicated to human arm endpoint impedance measurement in a cyclic task with physical interactions. Understanding human endpoint impedance adaptation during a cyclic task in interaction with the environment can provide insights for the design of innovative robot controllers for collaborative robotics both in manufacturing and service contexts. The ball bouncing benchmark task, classical in human movement science, is selected here for its simplicity, yet representativeness of handeye synchronization and intermittent physical interactions. For a better control over the environmental conditions, the task is performed in a simulated environment, where the paddle is actuated by the human participant by interacting with an admittance-controlled collaborative robotic arm. First experimental feasibility and identification results are presented.

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“…In addition, several studies have been carried out in which joint impedance identification was tested in simulation or in simplified experimental conditions where the joint of interest was perturbed while the subjects maintained a stationary position. A valuable example is given in [5], where a complex hybrid LTV estimation technique was developed to identify the intrinsic and reflexive properties of the ankle impedance, giving a full picture of the neuromuscular processes affecting the mechanical properties of the joint. The method is currently applied for human joint impedance identification during experimental conditions where the periodicity of the human behavior is assumed to repeatable.…”

Section: Introductionmentioning

confidence: 99%

Locally Periodic Kernel-Based Regression to Identify Time-Varying Ankle Impedance During Locomotion: a Simulation Study

Cavallo

Schouten

Lataire

2020

2020 42nd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Human joint impedance is a fundamental property of the neuromuscular system and describes the mechanical behavior of a joint. The identification of the lower limbs' joints impedance during locomotion is a key element to improve the design and control of active prostheses, orthoses, and exoskeletons. Joint impedance changes during locomotion and can be described by a linear time-varying (LTV) model. Several system identification techniques have been developed to retrieve LTV joint impedance, but these methods often require joint impedance to be consistent over multiple gait cycles. Given the inherent variability of neuromuscular control actions, this requirement is not realistic for the identification of human data.Here we propose the kernel-based regression (KBR) method with a locally periodic kernel for the identification of LTV ankle joint impedance. The proposed method considers joint impedance to be periodic yet allows for variability over the gait cycles. The method is evaluated on a simulation of joint impedance during locomotion. The simulation lasts for 10 gait cycles of 1.4 s each and has an output SNR of 15 dB. Two conditions were simulated: one in which the profile of joint impedance is periodic, and one in which the amplitude and the shape of the profile slightly vary over the periods. A Monte Carlo analysis is performed and, for both conditions, the proposed method can reconstruct the noiseless simulation output signal and the profiles of the time-varying joint impedance parameters with high accuracy (mean VAF ~ 99.9% and mean normalized RMSE of the parameters 1.33-4.06%). The proposed KBR method with a locally periodic kernel allows for the identification of periodic time-varying joint impedance with cycle-to-cycle variability.

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Unbiased Estimation of Human Joint Intrinsic Mechanical Properties During Movement

Cited by 12 publications

References 40 publications

Investigating the Muscular and Kinematic Responses to Sudden Wrist Perturbations During a Dynamic Tracking Task

Investigating the Muscular and Kinematic Responses to Sudden Wrist Perturbations During a Dynamic Tracking Task

Interactive robotics for human impedance estimation in a rhythmic task

Locally Periodic Kernel-Based Regression to Identify Time-Varying Ankle Impedance During Locomotion: a Simulation Study

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