On the Effect of Muscular Cocontraction on the 3-D Human Arm Impedance

Patel, Himanshu K.; O'Neill, Gerald D.; Artemiadis, Panagiotis

doi:10.1109/tbme.2014.2323938

Cited by 26 publications

(16 citation statements)

References 23 publications

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“…So, it is possible that the subjects co-adjusted the apparent stiffness with the RC drift to keep the hand motionless. This required increasing k , possibly by changing the level of muscle co-contraction (Franklin & Miller, 2003; Darainy, Malfait, Gribble, Towhidkhah, & Ostry, 2004; Patel, O’Neill, & Artemiadis, 2014). We did not record muscle activation levels, which may be viewed as a drawback of the study.…”

Section: Discussionmentioning

confidence: 99%

Characteristics of Unintentional Movements by a Multijoint Effector

Zhou¹,

Zhang²,

Latash³

2015

Journal of Motor Behavior

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We explored the phenomenon of unintentional changes in the equilibrium state of a multi-joint effector produced by transient changes in the external force. The subjects performed a positionholding task against a constant force produced by a robot and were instructed not to intervene voluntarily with movements produced by changes in the robot force. The robot produced a smooth force increase leading to a hand movement, followed by a dwell time. Then, the force dropped to its initial value leading to hand movement towards the initial position, but the hand stopped short of the initial position. The undershoot magnitude increased linearly with the peak hand displacement and exponentially with dwell time (time constant of about 1 s). For long dwell times, the hand stopped at about half the total distance to the initial position. We interpret the results as consequences of a drift of the referent hand coordinate. Our results provide support for backcoupling between the referent and actual body configurations during multi-joint actions and produce the first quantitative analysis of this phenomenon. This mechanism can also explain the phenomena of "slacking" and force drop after turning visual feedback off during accurate force production task.

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Section: Discussionmentioning

confidence: 99%

Characteristics of Unintentional Movements by a Multijoint Effector

Zhou¹,

Zhang²,

Latash³

2015

Journal of Motor Behavior

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“…Also, findings from Darainy, Malfait, Gribble, Towhidkhah, and Ostry (2004) were shown to be consistent with the idea that arm stiffness is controlled through the use of at least two independent co-contraction commands. More recently, Patel, O'Neill, and Artemiadis (2014) investigated the three-dimensional (3D) case and found an anisotropic increase of arm stiffness due to muscle co-contraction, underlying the directional tuning of endpoint stiffness to introduced instability, as demonstrated in previous literature for lower dimensions (Franklin et al, 2007).…”

Section: Prior Research On Mechanical Impedance Estimationmentioning

confidence: 66%

A Model for Operator Endpoint Stiffness Prediction during Physical Human-Robot Interaction

Moualeu

Ueda

2015

Journal of Human-Robot Interaction

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Physical contact established during interaction between a human operator and a haptic device creates a coupled system with stability and performance characteristics different than its individual subsystems taken in isolation. Proper incorporation of operator dynamics in physical human-robot interaction (pHRI) conditions requires knowledge of system variables and parameters, some of which are not directly measurable. Operator endpoint impedance, for instance, cannot be directly measured in typical haptic control conditions. Several endpoint impedance estimation techniques have been explored in previous literature, based on measured kinematics and/or other correlated metrics. Arm muscle activity, measured through surface electromyography (sEMG), has been used in previous literature to estimate endpoint stiffness, which is the static component of impedance. Co-activation (co-contraction) of antagonistic arm muscles forming a pair around a joint is known to be the driving factor in modulation of endpoint stiffness. However, previous work employing muscle co-contraction to predict endpoint stiffness has mainly been absent, due in part to the inefficacy of operator models to incorporate muscle co-activation into the prediction scheme. The current study proposes a method for prediction of operator endpoint stiffness based on measured co-contraction levels of a select group of muscles. The proposed methodology incorporates an upper extremity musculoskeletal model that accounts for muscle redundancy and the role of muscle co-contraction on arm stiffness modulation. The study hypothesizes that a free parameter, currently known in the literature to represent the nullspace of the mapping between muscle forces and joint torques, is a random variable of which probability density function can be estimated. Changes in this parameter directly affect changes in operator endpoint stiffness. Ten healthy subjects were asked to resist perturbations induced by a one degree of freedom (DOF) haptic paddle device while measurements, including muscle activities of four arm muscles, were being carried out. Direct derivation of stiffness values at the endpoint was compared to simulated endpoint stiffness values obtained using the proposed predictive methodology. Ten out of forty prediction trials resulted in a statistically significant correlation between predicted and actual stiffness values. Impressive stiffness prediction results, with over 99% peak accuracy in value, were found in only one trial using a combination of the proposed method along with a standard static optimization method for muscle force computation. Though the possibility of using a probabilistic approach to stiffness prediction was shown, robustness and generalizability of the proposed approach to multi-DOF systems remain to be addressed.

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“…The human limb's endpoint stiffness depends on posture and viscoelastic limb properties. There are various biomechanical studies that provide stiffness measurements [28]- [30], as well as models that provide the relationships between relevant variables [31]- [33]. The endpoint stiffness matrix can be calculated form the joint stiffness matrix as…”

Section: B Human Limb Stiffnessmentioning

confidence: 99%

“…Using the Hmodel from (18) in a closed-loop form and modified to include the coupling effect (see Fig. 11), we derive the closed-loop transfer function between human-induced external force F h * and human arm (slave device) motionẋ h aṡ x h F h * = h 22 Z e + 1 (h 22 Z e + 1) (Z h + h 11 ) − h 12 h 21 Z e (30) where H matrix elements, which include the gain (stiffness) of the slave impedance controller K s , are dependent on Z h . If the human impedance and the environment impedance are known, the derived closed-loop transfer function can be used to analyze the stability with the Root-Locus method [11], [24], [51] or the Nyquist method [7], [11], [51].…”

Section: B Stabilitymentioning

confidence: 99%

Analysis of Coupling Effect in Human-Commanded Stiffness During Bilateral Tele-Impedance

2021

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Tele-impedance augments classic teleoperation by enabling the human operator to actively command remote robot stiffness in real-time, which is an essential ability to successfully interact with the unstructured and unpredictable environment. However, the literature is missing a study on benefits and drawbacks of different types of stiffness command interfaces used in bilateral tele-impedance. In this article, we introduce a term called coupling effect, which pertains to the coupling between human-commanded stiffness going to the remote robot and force feedback coming from the remote robot. We hypothesize that, whenever the operator's commanded stiffness and force feedback are subject to coupling effect (e.g., muscle activity based stiffness command interfaces), force feedback can invoke involuntary changes in the commanded stiffness due to human reflexes. Although the coupling effect takes away some degree of the operator's control over the commanded stiffness, these involuntary changes can be either beneficial (e.g., during position tracking) or detrimental (e.g., during force tracking) to the task performance on the remote robot side. We examined the coupling effect in an experimental study with 16 participants, who performed position and force tracking tasks by using both coupled type (muscle activity based) and decoupled type (external device based) of interface. The results demonstrate a benefit of the coupling effect when the remote robot is operating in presence of unexpected force perturbations, where lower absolute error in position tracking task was observed. On the other hand, the decoupled type of interface is beneficial for force tracking tasks on the remote robot side, such as establishing or maintaining a stable contact with objects. However, the coupling effect negatively influences the commanding of reference stiffness to the remote robot in both position and force tracking tasks for the coupled type of interface, compared to the decoupled type of interface, which is not affected.

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On the Effect of Muscular Cocontraction on the 3-D Human Arm Impedance

Cited by 26 publications

References 23 publications

Characteristics of Unintentional Movements by a Multijoint Effector

Characteristics of Unintentional Movements by a Multijoint Effector

A Model for Operator Endpoint Stiffness Prediction during Physical Human-Robot Interaction

Analysis of Coupling Effect in Human-Commanded Stiffness During Bilateral Tele-Impedance

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