Learning to Control Arm Stiffness Under Static Conditions

Darainy, Mohammad; Malfait, Nicole; Gribble, Paul L.; Towhidkhah, Farzad; Ostry, David J.

doi:10.1152/jn.00596.2004

Cited by 83 publications

(83 citation statements)

References 20 publications

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“…Feedback responses to transient perturbations of endpoint position can be large (Krutky et al 2010;Perreault et al 2008), and protocols that use transient perturbations to estimate endpoint stiffness (Burdet et al 2001;Darainy et al 2004;Franklin et al 2007) may not be well-represented by our model. Deviations between our model predictions and stiffness estimates made using transient perturbations may be useful for beginning to assess reflex contributions to the regulation of endpoint stiffness.…”

Section: Discussionmentioning

confidence: 97%

“…Actively controlled mechanisms at a specific arm posture include the intrinsic properties of the muscles within the arm, which are dependent on their steady state or feedforward activation, and transient changes in muscle activation that may occur via feedback pathways such as stretch reflexes or voluntary responses to the imposed displacements. Although numerous studies have characterized the behavioral characteristics of endpoint stiffness (Burdet et al 2001;Darainy et al 2004;Franklin et al 2003;Franklin et al 2007;Gomi and Osu 1998;Perreault et al 2001;Tsuji et al 1995) and the feedback responses that may modulate this stiffness (Krutky et al 2010;Perreault et al 2008), few have directly assessed which muscle properties contribute most to the stiffness properties of an entire limb. This knowledge is essential for understanding how changes in neural activation alter limb stiffness or how impairments to muscular or neuromotor systems may impact the ability to regulate stiffness in a contextually appropriate manner.…”

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confidence: 99%

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Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

Murray

Perreault

2011

Journal of Neurophysiology

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Hu X, Murray WM, Perreault EJ. Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm. J Neurophysiol 105: 1633-1641, 2011. First published February 2, 2011 doi:10.1152/jn.00537.2010.-The mechanical properties of the human arm are regulated to maintain stability across many tasks. The static mechanics of the arm can be characterized by estimates of endpoint stiffness, considered especially relevant for the maintenance of posture. At a fixed posture, endpoint stiffness can be regulated by changes in muscle activation, but which activation-dependent muscle properties contribute to this global measure of limb mechanics remains unclear. We evaluated the role of muscle properties in the regulation of endpoint stiffness by incorporating scalable models of muscle stiffness into a three-dimensional musculoskeletal model of the human arm. Two classes of muscle models were tested: one characterizing short-range stiffness and two estimating stiffness from the slope of the force-length curve. All models were compared with previously collected experimental data describing how endpoint stiffness varies with changes in voluntary force. Importantly, muscle properties were not fit to the experimental data but scaled only by the geometry of individual muscles in the model. We found that forcedependent variations in endpoint stiffness were accurately described by the short-range stiffness of active arm muscles. Over the wide range of evaluated arm postures and voluntary forces, the musculoskeletal model incorporating short-range stiffness accounted for 98 Ϯ 2, 91 Ϯ 4, and 82 Ϯ 12% of the variance in stiffness orientation, shape, and area, respectively, across all simulated subjects. In contrast, estimates based on muscle force-length curves were less accurate in all measures, especially stiffness area. These results suggest that muscle short-range stiffness is a major contributor to endpoint stiffness of the human arm. Furthermore, the developed model provides an important tool for assessing how the nervous system may regulate endpoint stiffness via changes in muscle activation. musculoskeletal model WE COMMONLY USE OUR HANDS to move and manipulate objects in different environments. Many of these tasks tend to destabilize arm posture (Rancourt and Hogan 2001). Nevertheless, they can be completed because the central nervous system regulates the mechanical properties of the arm to compensate for these instabilities, usually ensuring that the coupled system of the arm and its environment remains stable so that posture can be maintained (McIntyre et al. 1996). Understanding how this regulation occurs and the relative contributions of the nervous system and the intrinsic biomechanics of the arm remains an important problem in motor control.

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Section: Discussionmentioning

confidence: 97%

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confidence: 99%

Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

Murray

Perreault

2011

Journal of Neurophysiology

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“…Darainy et al (2004) through their measurements have shown that joint stiffness has a dynamic property and changes in the course of the movement. However, for the sake of simplicity, in this study the joint stiffness is assumed to be constant during movement, which implicitly means that the co-activation command is also supposed to be constant.…”

Section: Central Motor Commands and Joint Torquesmentioning

confidence: 99%

MODEM: a multi-agent hierarchical structure to model the human motor control system

et al. 2009

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In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a "gate selector" block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM).

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“…For example, when hitting a volley in tennis, the path followed by the arm before the event is not as critical as the precision of position at the point of contact with the ball [36]. It has been shown that any task learning requires the central nervous system to identify the input-output characteristics of the motor system for that specific task [8,21,35]. Thus, in the tennis volleying example, the central nervous system has to learn to carefully modulate racquet position and stiffness to control ball trajectory after impact.…”

Section: Introductionmentioning

confidence: 99%

Task Performance is Prioritized Over Energy Reduction

Balasubramanian

Howe

Matsuoka

2009

IEEE Trans. Biomed. Eng.

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Abstract-The objective of this study was to characterize the temporal relationship between hand stiffness and task performance during adaptation to a brief contact task that required precision at the time of contact. The experiment required subjects to control the vertical position of a paddle on a computer display by grasping a robot's instrumented handle, with the goal of intercepting a virtual ball within 1 mm from the paddle center. A force transient was applied to the hand immediately after the ball-paddle impact to estimate the intrinsic hand impedance. There were two main results: (1) More trials were required for a brief contact task to find a low-energy strategy when compared with tasks that received feedback through the entire movement trajectory. (2) When the whole course of adaptation is long for brief contact tasks, viscoelastic forces were increased to achieve the task goal before the energy reduction initiated. Also, as the accuracy requirement was increased by changing the gain between handle and paddle motion through visual amplification, peak stiffness increased and occurred later, indicating that higher energy strategies are used for longer when the task's accuracy requirements were increased. These results indicated that task performance may be prioritized over energy reduction for a brief contact task.

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Learning to Control Arm Stiffness Under Static Conditions

Cited by 83 publications

References 20 publications

Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

Muscle short-range stiffness can be used to estimate the endpoint stiffness of the human arm

MODEM: a multi-agent hierarchical structure to model the human motor control system

Task Performance is Prioritized Over Energy Reduction

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