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Models of recruitment and rate coding organization in motor-unit pools.
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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“Firing rates were excluded from further analysis when the coefficient of variation in mean firing rate was greater than 30% or when the number of firings was lower than two, i.e., one firing (Fuglevand et al., 1993).”
From Effect of resistance training and fish protein intake on motor unit firing pattern and motor function of elderly (2018) · cites this paper as Fuglevand et al., 1993
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Urh, F., Strnad, D., Clarke, A., Farina, D., & Holobar, A. (2020) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-64610-3_102 · Cited by 1
Kasawara, K. T., Delafontaine, A., Tajali, S., Goligher, E. C., Brochard, L. J., Artico, R., & Reid, W. D. (2020) · Cardiopulmonary Physical Therapy Journal · doi:10.1097/cpt.0000000000000158 · Cited by 1
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Saini, H., & Röhrle, O. (2022) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-031-10015-4_20 · Cited by 1
Messaoudi, N., Belkacem, S., & Bekka, R. E. (2023) · Journal of Instrumentation · doi:10.1088/1748-0221/18/03/p03041 · Cited by 1
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Kunugi, S., Nakagoshi, A., Kawabe, K., & Watanabe, K. (2023) · European Journal of Applied Physiology · doi:10.1007/s00421-023-05196-2 · Cited by 1
“2016) and individual MUs with discharge rates showing a coefficient of variation (CoV) of over 30% (Fuglevand et al. 1993) were discarded.”
BackgroundMuscle force estimation using a measure of muscle activation extracted from surface EMG
Istenič, R., Holobar, A., Gazzoni, M., et al. (2007) · Cited by 1
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Kohn, A. F. (2009) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-642-03889-1_53 · Cited by 1
“Initially, the results will be compared with those described in the literature using the phenomenological model of [8].”
BackgroundResultAge-associated Differences in Discharge Rate Modulation of Human Motor Units
Pascoe, M. A. (2010) · CU Scholar (University of Colorado Boulder) · Cited by 1
Barrett, J. M. (2016) · UWSpace (University of Waterloo) · Cited by 1
Strategy for control of muscle force using a 3D multi electrode array in intraneural stimulation
Frieswijk, T. A., Dijkstra, F., & Rutten, W. L. C. (2002) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.1996.657029 · Cited by 1
Viljoen, S. (2005) · UpSpace Institutional Repository (University of Pretoria) · Cited by 1
Relationship Between Metabolic By-Products and Nervous System Failure/Fatigue
Farney, T. M. (2016) · doi:10.31390/gradschool_dissertations.1133 · Cited by 1
Hagen, D. A., & Valero‐Cuevas, F. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.08.194381 · Cited by 1
“It is important to note that our analysis has implications to most lumped-parameter models (e.g., Hill-type models, [27]) and population-of-muscle-fibers models (e.g., the Fuglevand model, [28]) because such models tend to ignore tendon mechanics and consider pennation angles to be negligible.”
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“This can be considered as a proxy for the EMG signal generated by the muscles during contraction ( [62]).”
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Maksymenko, K., Clarke, A. K., Guerra, I. M., Deslauriers‐Gauthier, S., & Farina, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.06.07.447390 · Cited by 1
“Linear or non-linear 474 rate coding models can be used [45, 46, 47].”
BackgroundNeuromechanics of the Initial Phase of Eccentric Contraction‐Induced Muscle Injury
Grabiner, M. D. (2000) · Wiley eBooks · doi:10.1002/9780470693797.ch28 · Cited by 1
Modelo computacional multiescala do sistema neuromuscular humano
Fernandes, H. S. (2018) · doi:10.47749/t/unicamp.2018.1061775 · Cited by 1
Onset detection for surface electromyography signals
Dai, F. (2010) · doi:10.22215/etd/2010-09204 · Cited by 1
Rohlén, R., Lubel, E., & Farina, D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.01.18.576300 · Cited by 1
Bilateral Grasp Force Coordination in Young and Old Adults.
Conti, G. E. (2007) · Deep Blue (University of Michigan)
Vuorokauden eri aikoina suoritetun voimaharjoittelun vaikutus hypertrofiaan ja voimaan
Lind, M. (2005) · Jyväskylä University Digital Archive (University of Jyväskylä)
Cowling, B. L. (2015) · Scholarship@Western (Western University)
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Ryan, K. (2013) · Scholarship@Western (Western University)
Motor Control Information Extracted from Surface EMG as Muscle Force Estimation
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Holobar, A., Gazzoni, M., Farina, D., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-73044-6_28
Hamamoto, A. N. (2013) · Digital Library of Theses and Dissertations (Universidade de São Paulo) · doi:10.11606/d.5.2013.tde-24062013-102052
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Englot, B. (2007) · DSpace@MIT (Massachusetts Institute of Technology)
REGULATION OF FES PARAMETERS FOR REDUCED MUSCLE FATIGUE IN LONG TERM SPINAL CORD INJURED INDIVIDUALS
Joghtaei, M. (2014) · UPT. Syiah Kuala University Library (Syiah Kuala University)
Callahan, D. M. (2019) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · doi:10.7275/pxne-a233
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Maltenfort, M., Markin, S. R., Lemay, M. A., et al. (2004) · doi:10.1109/iembs.2003.1279736
Maximal motor unit discharge rates of the medial and lateral gastrocnemeii of young males
Graham, M. (2014) · Scholarship@Western (Western University)
The influence of knee angle on human quadriceps femoris performance
Kooistra, R. D. (2007) · Digital Academic REpository of VU University Amsterdam (Vrije Universiteit Amsterdam)
Kutch, J. J., Suresh, N. L., Kuo, A. D., et al. (2006) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2006.377671
“Motor unit spike trains were generated independently with a constant coefficient of variation in their inter-spike interval of 20% [8].”
BackgroundMethodEndô, H., & Kawahara, K. (2011) · European Journal of Applied Physiology · doi:10.1007/s00421-011-1903-6
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Ye, W., Liu, S. Q., & Zeng, Y. (2013) · Neurophysiology · doi:10.1007/s11062-013-9383-4
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Hashemi, J., Morin, E. L., Mousavi, P., et al. (2012) · doi:10.1109/embc.2012.6346619
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BackgroundStudy of non-synchronized EMG epochs using a simple synchronizing mechanism in time domain
Afsheen, A. U., Qazi, B. S. A., & Malik, C. K. U. H. (2008) · International Symposium on Communications, Control and Signal Processing · doi:10.1109/isccsp.2008.4537441
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Singh, N. B. (2013) · edoc Publication server (Humboldt University of Berlin) · doi:10.18452/16772
Farina, D., & Merletti, R. (2005) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2001.1020410
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Yakub, F., Khudzari, A. Z. M., & Mori, Y. (2014) · Springer eBooks · doi:10.1007/978-4-431-54439-5_6
Hypothesis testing with a computer model for force production in muscle
Erim, Z., & Aghera, A. (2005) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2001.1020401
Coherence between motor unit discharge patterns investigated using a motoneuron model
Lowery, M. M., Myers, L. J., & Erim, Z. (2005) · doi:10.1109/cne.2005.1419605
A Stochastic Model for Assessing Synchronization among Spike Trains in a Population of Motor Neurons
Xiong, X., Yao, W., Tan, M., et al. (2003) · Methodology And Computing In Applied Probability · doi:10.1023/a:1026239320927
Preliminary results of EMG amplitude estimation with a muscle twitch model
Choi, C., & Kim, J. W. (2009) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2009.5109399
EMGextraction of neural strategies from the surface
Farina, D., Merletti, R., Enoka, R. M., et al. (2015)
Multichannel Blind Source Separation Using
Zazula, D. (2007)
SISTEMA DE SIMULAÇÃO DE CIRCUITOS NEURONAIS DA
Espinhal, M. (2007)
Farina, D., Gazzoni, M., Camelia, F., et al. (2013)
surface electromyogram Influence of amplitude cancellation on the simulated
Keenan, K. G., Farina, D., Maluf, K. S., Merletti, R., Roger, M., Duchateau, J., Baudry, S., Enoka, R. M., Coelho, A., Cannon, D., et al. (2015)
Calder, K. M. (2009) · QSpace (Queen's University Library)
Effects of Motor Activation Strategies on the Relationship Between EMG and Force
Day, S. J., Hulliger, M., Morrow, W. M., et al. (1995) · Springer eBooks · doi:10.1007/978-1-4615-1935-5_22
On the nature of stopping a voluntary action
McGarry, J. T. (2009) · Library and Archives Canada (Government of Canada) · doi:10.14288/1.0077250
MOTOR UNIT ACTIVITY AND NEUROMUSCULAR FUNCTION AFTER EXERCISE-INDUCED DAMAGE TO ELBOW FLEXOR MUSCLES
Dartnall, T. J. (2010) · Adelaide Research & Scholarship (AR&S) (University of Adelaide)
Introduction to electric neurophysiology in rehabilitation medicine.
Saito, E. (1995) · リハビリテーション医学 : 日本リハビリテーション医学会誌 · doi:10.2490/jjrm1963.32.571
Electroelastic polymer actuators
Babič, M., Vertechy, R., Berselli, G., et al. (2008)
Identificação de sistemas para o estudo de controle motor.
Watanabe, R. N. (2016) · doi:10.11606/t.3.2016.tde-27062016-162303
Dideriksen, J. L., Enoka, R. M., & Farina, D. (2011)
Bawa, P. N. S., & Jones, K. E. (2014) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-4666-6090-8.ch001
Koenig, A. C. (2006) · UPT. Syiah Kuala University Library (Syiah Kuala University)
Inference of computational models of tendon networks via sparse experimentation
Kurse, M. U. (2012) · University of Southern California Digital Library · doi:10.25549/usctheses-c3-65096
A Novel Measure of Motor Unit Action Potential Variability in Nonstationary Surface Electromyograms
Glaser, V., & Holobar, A. (2016) · Biosystems & biorobotics · doi:10.1007/978-3-319-46669-9_23
DOES FISHER INFORMATION CONSTRAIN HUMAN MOTOR CONTROL?
Harris, C. M. (2009) · International Joint Conference on Computational Intelligence · doi:10.5220/0002284704140420
“In particular, the estimator and its error are measurable as mean output force and variance (or a filtered version such as effector position), which can be approximated as the sum of individual motor unit forces (Fuglevand et al., 1993).”
BackgroundBryanton, M. A. (2016) · uO Research (University of Ottawa) · doi:10.20381/ruor-327
Brown, S. H. M. (2008) · UWSpace (University of Waterloo)
characteristics impedes progress on understanding the control of neuromuscular
Enoka, R. M., & Duchateau, J. (2015)
Relationship between Surface and Indwelling EMG Spike Shape Measures
Parro, J. J. (2014) · Brock University Digital Repository (Brock University)
Surface EMG Examination of Amyotrophic Lateral Sclerosis
Nezhad, F. J. (2015) · Figshare
Neural drive to human respiratory muscles
Saboisky, J. P. (2008) · UNSWorks (UNSW Sydney) · doi:10.26190/unsworks/17913
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Jegatheeswaran, G. (2012) · The Atrium (University of Guelph)
musclescontractions performed with the dorsiflexor Discharge properties of motor units during steady
Jesunathadas, M., Klass, M., Duchateau, J., & Enoka, R. M. (2012)
Motor unit diversity during elbow flexion
Adams, Z. J., Akins, R. E., & Buchanan, T. S. (2017) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2017.8008283
“Models have been created that attempt to identify these solutions [1]–[3], but no clear set of optimization parameters have emerged.”
BackgroundSousa, A. S. P. D. (2013) · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · doi:10.34626/9k0s-5518
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Dridi, A., Mtimet, J., & Yacoub, S. (2017) · International Conference on Information and Digital Technologies (IDT) · doi:10.1109/dt.2017.8012144
Precise assessment of HD-sEMG grid misalignment using nonlinear correlation
Khoury, R. A., Diab, A., Carriou, V., et al. (2017) · International Conference on Advances in Biomedical Engineering (ICABME) · doi:10.1109/icabme.2017.8167533
“For this reason, a more precise and sensitive grid alignment techniques are needed for shape analysis of HD-sEMG data, and muscle force estimation [5].”
BackgroundTowards realistic HPC models of the neuromuscular systemPreprint
Bradley, C. P., Emamy, N., Ertl, T., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.03211
“Moreover, those models can be directly linked to motor neuron models either phenomenologically [17, 12] or biophysically [9, 34] to further investigate the relationship between neural and mechanical behavior.”
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Chaytor, C. (2017) · Memorial University Research Repository (Memorial University)
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“2) Human Recorded Data: We also collected a real data set recording from a healthy young male at the Fuglevand Laboratory [16] using a rack-mounted electro-physiological recording system CED [2].”
MethodSelf-citationKranjec, J., & Holobar, A. (2018) · Biosystems & biorobotics · doi:10.1007/978-3-030-01845-0_202
Urh, F., & Holobar, A. (2018) · Biosystems & biorobotics · doi:10.1007/978-3-030-01845-0_227
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“We assume that the weighted neural activity directly drives the musculoskeletal system. c. Musculoskeletal Dynamics: Considerable work has been done on modeling the forward dynamics of the arm [7].”
BackgroundŠavc, M., Glaser, V., & Holobar, A. (2018) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2018.8513670
“The model proposed in [14] was used to generate MU firing patterns at different excitation levels.”
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Shaik, A. M. (2018) · RIT Scholar Works (Rochester Institute of Technology)
Mani, D. (2018) · CU Scholar (University of Colorado Boulder)
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Feeney, D. F. (2018) · CU Scholar (University of Colorado Boulder)
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Ma, S., Chen, C., Lv, B., et al. (2019) · doi:10.1109/embc.2019.8857676
“The recruitment thresholds (RTE) of the MUs followed the distribution described in [13].”
BackgroundMethodInfluentialArtemenko, T. G., Gotovtsev, I. I., & Artemenko, E. V. (2019) · International Volga Region Conference on Economics, Humanities and Sports (FICEHS) · doi:10.2991/aebmr.k.200114.214
“One of the first biomechanical principles, significant for the analysis of an athlete’s movement, was considered to be the indicator of changing the power impulse, its alternation and preservation, the combination of separate impulses, and their counteraction [18].”
Background
Metadata
- Authors
- Fuglevand, A. J., Winter, D. A., & Patla, A. E.
- Affiliations
- University of Waterloo (CA)
- 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
- 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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