Cited by 988Muscle activation and electromyography studiesMuscle Physiology and DisordersMotor Control and Adaptation

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Models of recruitment and rate coding organization in motor-unit pools.

Fuglevand, A. J., Winter, D. A., Patla, A. E.·1993·Journal of Neurophysiology

DOI 10.1152/jn.1993.70.6.2470

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Abstract

1. Isometric muscle force and the surface electromyogram (EMG) were simulated from a model that predicted recruitment and firing times in a pool of 120 motor units under different levels of excitatory drive. The EMG-force relationships that emerged from simulations using various schedules of recruitment and rate coding were compared with those observed experimentally to determine which of the modeled schemes were plausible representations of the actual organization in motor-unit pools. 2. The model was comprised of three elements: a motoneuron model, a motor-unit force model, and a model of the surface EMG. Input to the neuron model was an excitatory drive function representing the net synaptic input to motoneurons during voluntary muscle contractions. Recruitment thresholds were assigned such that many motoneurons had low thresholds and relatively few neurons had high thresholds. Motoneuron firing rate increased as a linear function of excitatory drive between recruitment threshold and peak firing rate levels. The sequence of discharge times for each motoneuron was simulated as a random renewal process. 3. Motor-unit twitch force was estimated as an impulse response of a critically damped, second-order system. Twitch amplitudes were assigned according to rank in the recruitment order, and twitch contraction times were inversely related to twitch amplitude. Nonlinear force-firing rate behavior was simulated by varying motor-unit force gain as a function of the instantaneous firing rate and the contraction time of the unit. The total force exerted by the muscle was computed as the sum of the motor-unit forces. 4. Motor-unit action potentials were simulated on the basis of estimates of the number and location of motor-unit muscle fibers and the propagation velocity of the fiber action potentials. The number of fibers innervated by each unit was assumed to be directly proportional to the twitch force. The area of muscle encompassing unit fibers was proportional to the number of fibers innervated, and the location of motor-unit territories were randomly assigned within the muscle cross section. Action-potential propagation velocities were estimated from an inverse function of contraction time. The train of discharge times predicted from the motoneuron model determined the occurrence of each motor-unit action potential. The surface EMG was synthesized as the sum of all motor-unit action-potential trains. 5. Two recruitment conditions were tested: narrow (limit of recruitment < 50% maximum excitation) and broad recruitment range conditions (limit of recruitment > 70% maximum excitation).(ABSTRACT TRUNCATED AT 400 WORDS)

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“With the MU model, we created a population of 120 MUs for each motor neuron pool whose recruitment thresholds were distributed according to their twitch properties (Fuglevand et al. 1993; Santello and Fuglevand 2004).”
EMG Comparison of Selected Ankle Rehabilitation Exercises
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Neural mechanisms underlying the recovery of voluntary control of motoneurons after paralysis with spinal cord stimulation
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“In particular, these properties help delineate the fundamental differences in motoneuron activation, revealing that motoneurons typically exhibit a relatively low rheobase (Fuglevand et al. 1993; Powers and Binder 1985).”
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“The recruitment of a population of MUs was based on the model proposed by Fuglevand et al. [34], recruitment thresholds with range between 0 and 50% of maximal excitation level.”
Method
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Lin, C., Chen, C., Cui, Z., & Zhu, X. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1306054 · Cited by 13
Long‐range correlations in motor unit discharge times at low forces are modulated by visual gain and age
Jordan, K., Jesunathadas, M., Sarchet, D. M., & Enoka, R. M. (2012) · Experimental Physiology · doi:10.1113/expphysiol.2012.067975 · Cited by 13
“Although many physiological time series, including force and motor unit discharge times, have been regarded as stochastic or random (Hubbard et al. 1967; Calvin & Stevens, 1968; Fuglevand et al. 1993), such time series do contain deterministic structure; the time points within the series are not independent of each ot…”
Background
Differences in EMG spike shape between individuals with and without non-specific arm pain
Calder, K. M., Gabriel, D. A., & McLean, L. (2008) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2008.11.015 · Cited by 13
Effects of Task Demands on Kinematics and EMG Signals during Tracking Tasks Using Multiscale Entropy
Wu, Y., & Song, R. (2017) · Entropy · doi:10.3390/e19070307 · Cited by 13
A model of the rat medial gastrocnemius muscle based on inputs to motoneurons and on an algorithm for prediction of the motor unit force
Raikova, R., Celichowski, J., Angelova, S., et al. (2018) · Journal of Neurophysiology · doi:10.1152/jn.00041.2018 · Cited by 13
“We used a common drive conception (Contessa et al. 2011; De Luca and Erim 1994; Fuglevand et al. 1993), which presumes that one and the same signal from the central nervous system controls all MUs of a muscle, and the recruitment and decruitment of MUs are determined predominantly by internal…”
MethodInfluential
Characterization of Spastic Ankle Flexors Based on Viscoelastic Modeling for Accurate Diagnosis
Shin, W.-S., Chang, H., Kim, S. J., et al. (2019) · International Journal of Control Automation and Systems · doi:10.1007/s12555-019-0245-8 · Cited by 13
Identification of the laterality of motor unit behavior in female patients with parkinson's disease using high‐density surface electromyography
Nishikawa, Y., Watanabe, K., Holobar, A., Maeda, N., Maruyama, H., & Tanaka, S. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.15099 · Cited by 16
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Tang, X., Zhang, X., Chen, M., Chen, X. '., & Chen, X. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3126752 · Cited by 13
Real-Time Finger Force Estimation Robust to a Perturbation of Electrode Placement for Prosthetic Hand Control
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Assessment of motor unit firing by high‐density surface electromyography detects motor neuronal hyperexcitable state in amyotrophic lateral sclerosis
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“(IFRs) with a CoV of over 30% were excluded from the analysis, as described in a previous study.(23) In the present study, identified MUs during ramp-up contraction were divided into four subgroups”
Method
Linking cortex and contraction—Integrating models along the corticomuscular pathway
Haggie, L., Schmid, L., Röhrle, O., Besier, T. F., McMorland, A. J. C., & Saini, H. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1095260 · Cited by 14
Simulations of high-density surface electromyograms in dynamic muscle contractions
Glaser, V., Farina, D., & Holobar, A. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037599 · Cited by 12
“For this reason, we used previously validated Fuglevand’s model [10] to calculate the MU firing patterns with MU firing rates ranging from 8 Hz at MU recruitment to 35 Hz at the maximal muscle excitation.”
Method
Improve computational efficiency and estimation accuracy of multi-channel surface EMG decomposition via dimensionality reduction
Ning, Y., Dias, N., Li, X., Jie, J., Li, J., & Zhang, Y. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103372 · Cited by 14
Analysis of the effects of firing rate and synchronization on spike-triggered averaging of multidirectional motor unit torque
Kutch, J. J., Suresh, N. L., Bloch, A. M., et al. (2007) · Journal of Computational Neuroscience · doi:10.1007/s10827-007-0023-0 · Cited by 13
“Since, to our knowledge, no models exists for the recruitment and rate-coding schedule for muscles other than the FDI, we assumed that each muscle had the same schedule as does the FDI model (Fuglevand et al. 1993).”
BackgroundMethodInfluential
Modified motor unit number index: A simulation study of the first dorsal interosseous muscle
Li, X., Nandedkar, S. D., & Zhou, P. (2015) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2015.11.002 · Cited by 13
“The mMUNIX and standard MUNIX were calculated from two motor unit firing rate strategies (“onion skin” strategy and reverse “onion skin” strategy), and from two motor unit recruitment ranges (40% excitation and 80% excitation) [21].”
BackgroundMethod
A model of the feline medial gastrocnemius motoneuron-muscle system subjected to recurrent inhibition
Uchiyama, T., Johansson, H., & Windhorst, U. (2003) · Biological Cybernetics · doi:10.1007/s00422-003-0413-y · Cited by 13
Motor unit identification in two neighboring recording positions of the human trapezius muscle during prolonged computer work
Zennaro, D., Läubli, T., & Krueger, H. (2003) · European Journal of Applied Physiology · doi:10.1007/s00421-003-0821-7 · Cited by 13
“A MU territory is considered as the circular crosssectional area in which all the fibers of a MU are located (Fuglevand et al. 1993; Stashuk 1993).”
Background
Muscle force and myoelectric manifestations of muscle fatigue in voluntary and electrically elicited contractions
Merletti, R., Afsharipour, B., Dideriksen, J., & Farina, D. (2016) · doi:10.1002/9781119082934.ch10 · Cited by 12
I-Spin live, an open-source software based on blind-source separation for real-time decoding of motor unit activity in humansPreprint
Rossato, J., Hug, F., Tucker, K. J., Gibbs, C., Lacourpaille, L., Farina, D., & Avrillon, S. (2023) · eLife · doi:10.7554/elife.88670 · Cited by 13
“Recruitment thresholds, firing rate/excitatory drive relations, and twitch parameters were assigned to each motor unit using the same procedure as Fuglevand et al., 1993 .”
Method
The History and Future of Neuromusculoskeletal Biomechanics
Lloyd, D. G., Jonkers, I., Delp, S. L., & Modenese, L. (2023) · Journal of Applied Biomechanics · doi:10.1123/jab.2023-0165 · Cited by 13
Advanced modeling method for quantifying cumulative subjective fatigue in mid-air interaction
Villanueva, A., Jang, S., Stuerzlinger, W., et al. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102931 · Cited by 12
“Thus, the flow rates could be different based on the current muscle capacity and task load (Fuglevand et al., 1993).”
Background
Motor unit synchronization during fatigue: A novel quantification method
Grönlund, C., Holtermann, A., Roeleveld, K., et al. (2007) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2007.07.012 · Cited by 12
A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography
Zhao, Y. C., & Li, D. (2012) · Science China Life Sciences · doi:10.1007/s11427-012-4400-1 · Cited by 12

Authors: Fuglevand, A. J., Winter, D. A., & Patla, A. E.
Affiliations: University of Waterloo (CA)
ORCID: Fuglevand, A. J.
Winter, D. A.
Venue: Journal of Neurophysiology
Publisher: American Physiological Society
Published: 1993-12-01
Volume: 70
Issue: 6
Pages: 2470-2488
ISSN: 0022-3077 (ISSN-L)
1522-1598
DOI: 10.1152/jn.1993.70.6.2470
OpenAlex: W1809394698
Cited by: 988
References: 0
Citations by year: 2026: 41 · 2025: 47 · 2024: 64 · 2023: 49 · 2022: 38 · 2021: 55 · 2020: 43 · 2019: 44 · 2018: 45 · 2017: 37 · 2016: 37 · 2015: 41 · 2014: 34 · 2013: 38 · 2012: 39
FWCI: 4.88
Type: journal article
Language: en
Open access: closed
Locations: Journal of Neurophysiology (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Muscle Physiology and Disorders
Life Sciences › Neuroscience › Cognitive Neuroscience › Motor Control and Adaptation
Keywords: motor unit recruitment, rate coding, surface electromyogram, EMG-force relationship, motor unit action potentials, action potential propagation velocity, recruitment threshold, motor unit territory, isometric force, voluntary muscle contraction
MeSH terms: Animals, Computer Simulation, Electromyography, Humans, Isometric Contraction, Models, Neurological, Motor Neurons, Muscles, Synaptic Transmission, Muscle Spindles, Recruitment, Neurophysiological

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Models of recruitment and rate coding organization in motor-unit pools.

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