A Neuro-Muscular Elasto-Dynamic Model of the Human Arm Part 2: Musculotendon Dynamics and Related Stress Effects

Moody, Cody B.; Barhorst, Alan A.; Schovanec, Lawrence

doi:10.1016/s1672-6529(08)60108-0

Cited by 7 publications

(10 citation statements)

References 19 publications

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“…We also provided a description of how to build and implement Bézier splines to represent the intrinsic force-length and force-velocity properties of muscle. The r 2 for each fitted curve was greater than 0.87, comparable to the r 2 values for curves from [ 34 , 37 – 42 ] fitted to the same experimental data from [ 47 – 48 ]. However, the Bézier splines improve upon these more commonly used curves by allowing greater control in replicating the physiological features found in experimental muscle data.…”

Section: Resultssupporting

confidence: 65%

“…A variety of different functions have been used to represent the intrinsic force-velocity and force-length relationships, including piecewise [ 33 – 36 ], polynomial [ 35 – 38 ], hyperbolic [ 39 – 40 ], trigonometric [ 41 – 42 ], logarithmic [ 40 ], and exponential [ 34 , 39 , 43 ] functions. There is typically a trade-off between accuracy and cost when choosing curves to model these intrinsic properties.…”

Section: Methods and Modelsmentioning

confidence: 99%

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A modelling approach for exploring muscle dynamics during cyclic contractions

2018

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Hill-type muscle models are widely used within the field of biomechanics to predict and understand muscle behaviour, and are often essential where muscle forces cannot be directly measured. However, these models have limited accuracy, particularly during cyclic contractions at the submaximal levels of activation that typically occur during locomotion. To address this issue, recent studies have incorporated effects into Hill-type models that are oftentimes neglected, such as size-dependent, history-dependent, and activation-dependent effects. However, the contribution of these effects on muscle performance has yet to be evaluated under common contractile conditions that reflect the range of activations, strains, and strain rates that occur in vivo. The purpose of this study was to develop a modelling framework to evaluate modifications to Hill-type muscle models when they contract in cyclic loops that are typical of locomotor muscle function. Here we present a modelling framework composed of a damped harmonic oscillator in series with a Hill-type muscle actuator that consists of a contractile element and parallel elastic element. The intrinsic force-length and force-velocity properties are described using Bézier curves where we present a system to relate physiological parameters to the control points for these curves. The muscle-oscillator system can be geometrically scaled while preserving dynamic and kinematic similarity to investigate the muscle size effects while controlling for the dynamics of the harmonic oscillator. The model is driven by time-varying muscle activations that cause the muscle to cyclically contract and drive the dynamics of the harmonic oscillator. Thus, this framework provides a platform to test current and future Hill-type model formulations and explore factors affecting muscle performance in muscles of different sizes under a range of cyclic contractile conditions.

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Section: Resultssupporting

confidence: 65%

Section: Methods and Modelsmentioning

confidence: 99%

A modelling approach for exploring muscle dynamics during cyclic contractions

2018

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“…The muscle contraction model ( Figure 3 ) defines both the active and the passive forces generated by the musculotendon unit. To describe this dynamic behaviour, a Hill-type muscle model is commonly used [ 3 , 9 , 11 , 13 , 14 , 18 , 19 , 30 ]. The tendon element, typically modelled as an elastic element, is modelled as a rigid tendon element (RTE) in this muscle contraction model.…”

Section: Methodsmentioning

confidence: 99%

“…In the absence of global solutions within the area of wearable elbow device control, a large number of motion models has been proposed [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. These models can be arranged along a spectrum based on the level of decomposition of human motion properties.…”

Section: Introductionmentioning

confidence: 99%

“…At one end of this spectrum, the dynamics of the human motor control system are treated as black-box models, such as in motion models built upon artificial neural networks [ 10 ], where inputs are mapped to outputs with limited internal knowledge of the properties. Towards the other end of the spectrum, the dynamics of the human motor control system are decomposed into sub-models, such as in the muscle-level model [ 3 , 9 , 11 , 12 ]. The general muscle-level model ( Figure 1 ) describes internal force production as the summation of forces generated by motor units making up a given musculotendon unit.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating Muscle Activation Models for Elbow Motion Estimation

Desplenter

Trejos

2018

Sensors

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Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand.

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Nonlinear passive tremor control of human arm

Savadkoohi

Lamarque

Goossaert

2021

Mechanical Systems and Signal Processing

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An academic model for passive vibration control of human arm suffering from essential tremors is proposed. The nonlinear uncontrolled system equations are linearised around arbitrary postural positions showing variations of linear natural frequencies as functions of postural positions. The coupled system, i.e. the model of the arm with the attached nonlinear absorber, is treated via a time multiple scale method leading to detection of fast and slow systems dynamics. These dynamics provide design tools for tuning parameters of the nonlinear absorber.

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A Neuro-Muscular Elasto-Dynamic Model of the Human Arm Part 2: Musculotendon Dynamics and Related Stress Effects

Cited by 7 publications

References 19 publications

A modelling approach for exploring muscle dynamics during cyclic contractions

A modelling approach for exploring muscle dynamics during cyclic contractions

Evaluating Muscle Activation Models for Elbow Motion Estimation

Nonlinear passive tremor control of human arm

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