Hill-type computational models of muscle-tendon actuators: a systematic review

Caillet, Arnault Hubert; Phillips, A.T.M.; Carty, Christopher P.; Farina, Dario; Modenese, Luca

doi:10.1101/2022.10.14.512218

Cited by 16 publications

(42 citation statements)

References 309 publications

(532 reference statements)

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“…These results clarify the directions for designing new surface grids of electrodes that could span across the entire surface of the muscle of interest while keeping a high density of electrodes, with IED as low as 2-4 mm. Identifying large sets of small and large motor units is relevant in many research areas related to motor control, such as the investigation of neural synergies (Hug et al, 2022), neuromuscular modelling (Caillet et al, 2022c), or human-machine interfacing (Farina et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

Caillet

Avrillon

Kundu

et al. 2023

Preprint

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The spinal motor neurons are the only neural cells whose individual activity can be non-invasively identified using grids of electromyographic (EMG) electrodes and source separation methods, i.e., EMG decomposition. In this study, we combined computational and experimental approaches to assess how the design parameters of grids of electrodes influence the number and characteristics of the motor units identified. We first computed the percentage of unique motor unit action potentials that could be theoretically discriminated in a pool of 200 simulated motor units when recorded with grids of various sizes and interelectrode distances (IED). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). Increasing both the density and the number of electrodes, as well as the size of the grids, increased the number of motor units that the EMG decomposition could theoretically discriminate, i.e., up to 82.5% of the simulated pool (range: 30.5-82.5%). Experimentally, the configuration with the largest number of electrodes and the shortest IED maximized the number of motor units identified (56 +/- 14; range: 39-79) and the percentage of low-threshold motor units identified (29 +/- 14%). Finally, we showed with a prototyped grid of 400 electrodes (IED: 2 mm) that the number of identified motor units plateaus beyond an IED of 2-4 mm. These results showed that larger and denser surface grids of electrodes help to identify a larger and more representative pool of motor units than currently reported in experimental studies.

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Section: Discussionmentioning

confidence: 99%

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

Caillet

Avrillon

Kundu

et al. 2023

Preprint

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“…Second, the muscle-related results obtained from MSK simulations are highly sensitive to some MSK parameters [43][44][45][46], including the muscle-specific maximum isometric force, tendon slack length, optimal fibre length, and the muscle moment arm. These parameters in the ARMs model may deviate from the specific subject, which will affect the estimation of muscle activation.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Ma,

Guerra,

Caillet

et al. 2023

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Neuromechanical studies investigate how the nervous system interacts with the musculoskeletal (MSK) system to generate volitional movements. Such studies have been supported by simulation models that provide insights into variables that cannot be measured experimentally and allow a large number of conditions to be tested before the experimental analysis. However, current simulation models of electromyography (EMG), a core physiological signal in neuromechanical analyses, are mainly limited to static contractions and cannot fully represent the dynamic modulation of EMG signals during volitional movements. Here, we overcome these limitations by presenting NeuroMotion, an open-source simulator that provides a full-spectrum synthesis of EMG signals during voluntary movements. NeuroMotion is comprised of three modules. The first module is an upper-limb MSK model with OpenSim API to estimate the muscle fibre lengths and muscle activations during movements. The second module is BioMime, a deep neural network-based EMG generator that receives nonstationary physiological parameter inputs, such as muscle fibre lengths, and efficiently outputs motor unit action potentials (MUAPs). The third module is a motor unit pool model that transforms the muscle activations into discharge timings of motor units. The discharge timings are convolved with the output of BioMime to simulate EMG signals during the movement. Here we also provide representative applications of NeuroMotion. We first show how simulated MUAP waveforms change during different levels of physiological parameter variations and different movements. We then show that the synthetic EMG signals during two-degree-of-freedom hand and wrist movements can be used to augment experimental data for regression. Ridge regressors trained on the synthetic dataset were directly used to predict joint angles from experimental data. NeuroMotion is the first full-spectrum EMG generative model to simulate human forearm electrophysiology during voluntary hand, wrist, and forearm movements. All intermediate variables are available, which allows the user to study cause-effect relationships in the complex neuromechanical system, fast iterate algorithms before collecting experimental data, and validate algorithms that estimate non-measurable parameters in experiments. We expect this full-spectrum model will complement experimental approaches and facilitate neuromechanical research.Author summaryNeuromechanical studies investigate how the nervous system and musculoskeletal system interact to generate movements. Such studies heavily rely on simulation models, which provide non-measurable variables to complement the experimental analyses. However, the simulation models of surface electromyography (EMG), the core physiological signal widely used in neuromechanical analyses, are limited to static conditions. We bridged this gap by proposing NeuroMotion, the first full-spectrum EMG simulator that can be used to generate EMG signals during voluntary movements. NeuroMotion integrates a musculoskeletal model, a neural network-based EMG generator, and an advanced motoneuron model. With representative applications of this simulator, we show that it can be used to investigate the variabilities of EMG signals during voluntary movement. We also demonstrate that the synthetic signals generated by NeuroMotion can be used to augment experimental data for regressing joint angles. We expect the functionality provided by NeuroMotion, which is provided open-source, will stimulate progress in neuromechanics.

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“…Understanding the workings of intra-muscular motion has broader implications than US interpretation and processing, such as in musculoskeletal modelling. Generally, a muscle is modeled as a single actuator [30], geometrically described as a line segment between its insertion points on the skeletal bodies [31], assuming all fibers have the same length and neuromuscular properties using multiscale simplifications [32]. Some muscle modeling developments have aimed for a more physiological underpinning by describing the dynamics of the individual MUs of the muscle, for example by using controlled pools of in-parallel mathematical models of individual MUs with artificial [29], [33]- [35] and experimentally generated [36] MU firing times.…”

Section: Discussionmentioning

confidence: 99%

“…However, both approaches assume no inter-unit connectivity between the modeled fiber actuators. In such cases, the relative motions between fibers are assumed to be independent and their generated forces are modelled to add linearly, which contradicts physiological findings [32], [39]. Experimental insights into the true impacts of the inter-relation between MUs could improve these models, and US detection methods could enable a precise mapping of MUs within the muscle volume.…”

Section: Discussionmentioning

confidence: 99%

Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation

Lubel

Sgambato

Rohlén

et al. 2023

Preprint

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Ultrasound (US) muscle image series can be used for peripheral human-machine interfacing based on global features, or even on the decomposition of US images into the contributions of individual motor units (MUs). With respect to state-of-the-art surface electromyography (sEMG), US provides higher spatial resolution and deeper penetration depth. However, the accuracy of current methods for direct US decomposition, even at low forces, is relatively poor. These methods are based on linear mathematical models of the contributions of MUs to US images. Here, we test the hypothesis of linearity by comparing the average velocity twitch profiles of MUs when varying the number of other concomitantly active units. We observe that the velocity twitch profile has a decreasing peak-to-peak amplitude when tracking the same target motor unit at progressively increasing contraction force levels, thus with an increasing number of concomitantly active units. This observation indicates non-linear factors in the generation model. Furthermore, we directly studied the impact of one MU on a neighboring MU, finding that the effect of one source on the other is not symmetrical and may be related to unit size. We conclude that a linear approximation is limiting the decomposition methods to decompose full velocity twitch trains from velocity images, highlighting the need for more advanced models and methods for US decomposition than those currently employed.

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Hill-type computational models of muscle-tendon actuators: a systematic review

Cited by 16 publications

References 309 publications

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

NeuroMotion: Open-source Simulator with Neuromechanical and Deep Network Models to Generate Surface EMG signals during Voluntary Movement

Non-linearity in motor unit velocity twitch dynamics: Implications for ultrafast ultrasound source separation

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