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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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Motor output is more variable during eccentric compared with concentric contractions
Christou, E. A., & Carlton, L. G. (2002) · Medicine & Science in Sports & Exercise · doi:10.1097/00005768-200211000-00013 · Cited by 55
Analysis and simulation of changes in emg amplitude during high-level fatiguing contractions
Lowery, M. M., & O'Malley, M. J. (2003) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2003.816078 · Cited by 55
“EMG models have been also been presented by several groups [38]–[40].”
MethodPetersen, E., & Rostalski, P. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00176 · Cited by 56
“Secondly, the classical Fuglevand model (Fuglevand et al., 1993) has been extended to reflect findings regarding the dependence of MU and muscle fiber properties on MU and fiber size.”
BackgroundMethodResultInfluentialA model of cerebrocerebello-spinomuscular interaction in the sagittal control of human walking
Jo, S., & Massaquoi, S. G. (2006) · Biological Cybernetics · doi:10.1007/s00422-006-0126-0 · Cited by 54
“…0.30 0.023 9.1 Gastrocnemius bi, knee flexor, 0.56 0.050(k), 30 (GC) ankle plantarflexor 0.040(a) Soleus (SO) mono, ankle plantarflexor 0.35 0.036 58 (Fuglevand and Winter 1993) where s is the Laplace variable, and then act = EC(s) (uα) (11) meaning that the filter is applied to uα that is the…”
BackgroundInfluentialZhou, P., Suresh, N. L., & Rymer, W. Z. (2007) · Annals of Biomedical Engineering · doi:10.1007/s10439-007-9329-3 · Cited by 54
The role of the neural stimulus in regulating skeletal muscle hypertrophy
Alix‐Fages, C., Vecchio, A. D., Baz-Valle, E., Santos‐Concejero, J., & Balsalobre‐Fernández, C. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-022-04906-6 · Cited by 54
“Muscle force is regulated by the central nervous system, which vary the activity of the motor units that comprise the muscle (Clamann 1993; Fuglevand et al. 1993).”
BackgroundTask-Dependent Modulation of Multi-Digit Force Coordination Patterns
Rearick, M. P., Casares, A., & Santello, M. (2003) · Journal of Neurophysiology · doi:10.1152/jn.00581.2002 · Cited by 54
“Both the neural and biomechanical architecture of finger muscles likely play a major role in the simultaneous scaling of normal forces at all digits and their temporal covariation.”
BackgroundMethodTutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices
Clancy, E. A., Morin, E. L., Hajian, G., & Merletti, R. (2023) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2023.102807 · Cited by 61
Effect of instruction, surface stability, and load intensity on trunk muscle activity
Bressel, E., Willardson, J. M., Thompson, B. J., & Fontana, F. E. (2008) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2008.10.006 · Cited by 53
“It would seem reasonable to assume that load intensity would increase EMG values in our study as there is a positive relationship between EMG activity levels and isometric force production (Fuglevand et al., 1993).”
BackgroundWatanabe, K., & Akima, H. (2009) · European Journal of Applied Physiology · doi:10.1007/s00421-009-1064-z · Cited by 53
Formento, E., Botros, P., & Carmena, J. M. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac35ac · Cited by 54
“Flexion and supination recruitment thresholds for all units spanned a wide range, distributed in agreement with the common model of motor unit frequency distribution skewing towards more lower-threshold units within a muscle [40] (figures 6(A) and (B)).”
BackgroundAyachi, F., Boudaoud, S., & Marque, C. K. (2014) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-014-1170-x · Cited by 52
Effect of spatial filtering on crosstalk reduction in surface EMG recordings
Mesin, L., Smith, S. T., Hugo, S., et al. (2008) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2008.05.006 · Cited by 52
“It corresponded to the fibre density in the MUs, which is about 10 times lower than fibre density in a muscle [29].”
Raikova, R., Celichowski, J., Pogrzebna, M., et al. (2006) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2006.01.005 · Cited by 52
“[12] have modeled the force increase during tetanus by introducing a gain factor in the function describing the twitch, i.”
How different afferent inputs control motoneuron discharge and the output of the motoneuron pool
Binder, M. D., Heckman, C. J., & Powers, R. K. (1993) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(93)90177-z · Cited by 52
Limitations of the surface electromyography technique for estimating motor unit synchronization
Yue, G. H., Fuglevand, A. J., Nordstrom, M. A., & Enoka, R. M. (1995) · Biological Cybernetics · doi:10.1007/bf00201424 · Cited by 54
Self-citationPracticing a Functional Task Improves Steadiness with Hand Muscles in Older Adults
Marmon, A. R., Gould, J. R., & Enoka, R. M. (2011) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3182100439 · Cited by 51
“Because the contributions of individual motor units to the net force exerted by a muscle are greatest at low forces (2), relatively minor decreases in motor unit discharge variability due to practicing the pegboard practice would be most effective at reducing the magnitude of the force fluctuations at the lowest targe…”
BackgroundRevill, A. L., & Fuglevand, A. J. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00419.2011 · Cited by 52
“To evaluate this possibility, we used an updated version of a motor unit pool model (Fuglevand et al. 1993) in which we selectively included or omitted each of these nonlinear properties in simulations of muscle contractions.”
BackgroundMethodResultInfluentialSelf-citationMechanical properties and neural control of human hand motor units
Fuglevand, A. J. (2011) · The Journal of Physiology · doi:10.1113/jphysiol.2011.215236 · Cited by 52
“Regardless of the method used, experimental work characterizing motor unit properties in humans and other mammals has been consistent with regard to two findings (see Fuglevand et al. 1993).”
ResultSelf-citationFactors responsible for force steadiness impairment with fatigue
Missenard, O., Mottet, D., & Perrey, S. (2009) · Muscle & Nerve · doi:10.1002/mus.21331 · Cited by 52
Role of across-muscle motor unit synchrony for the coordination of forces
Santello, M., & Fuglevand, A. J. (2004) · Experimental Brain Research · doi:10.1007/s00221-004-1975-1 · Cited by 51
Self-citationBoe, S. G., Stashuk, D. W., & Doherty, T. J. (2007) · Muscle & Nerve · doi:10.1002/mus.20784 · Cited by 51
Coordinated alpha and gamma control of muscles and spindles in movement and posture
Li, S., Zhuang, C., Hao, M., He, X., Márquez, J. C., Niu, C. M., & Lan, N. (2015) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2015.00122 · Cited by 55
“…is the sum of descending commands to αMN, C(t) represents background activation of αMN pools, τ is the time constant of excitation with the value 0.029 (sec), σ (t) represents the signal dependent noise, which is a Gaussian distributed random signal (Fuglevand et al., 1993; Jones et al., 2002).”
BackgroundInfluentialMUAP Number Estimates in Surface EMG: Template-Matching Methods and Their Performance Boundaries
Zhou, P., & Rymer, W. Z. (2004) · Annals of Biomedical Engineering · doi:10.1023/b:abme.0000032463.26331.b3 · Cited by 50
Analysis of the sEMG/force relationship using HD-sEMG technique and data fusion: A simulation study
Harrach, M. A., Carriou, V., Boudaoud, S., et al. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.02.003 · Cited by 50
Keenan, K. G., & Valero‐Cuevas, F. J. (2007) · Journal of Neurophysiology · doi:10.1152/jn.00577.2007 · Cited by 49
Detecting motor unit abnormalities in amyotrophic lateral sclerosis using high-density surface EMG
Nishikawa, Y., Holobar, A., Watanabe, K., Takahashi, T., Ueno, H., Maeda, N., Maruyama, H., Tanaka, S., & Hyngstrom, A. S. (2022) · Clinical Neurophysiology · doi:10.1016/j.clinph.2022.06.016 · Cited by 49
Capogrosso, M., Gandar, J., Greiner, N., Moraud, E. M., Wenger, N., Shkorbatova, P. Y., Евгеньевич, М. П., Minev, I. R., Lacour, S. P., & Courtine, G. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaa87a · Cited by 48
Aging and skeletal muscle force control: Current perspectives and future directions
Pethick, J., Taylor, M. J. D., & Harridge, S. D. R. (2022) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.14207 · Cited by 52
“92 Fluctuations in motor unit force when the unit is first recruited and discharging at low rates are greater in older adults 93 and this has been speculated to contribute to the age-associated difference in force fluctuations at low-intensities.”
BackgroundDartnall, T. J., Rogasch, N. C., Nordstrom, M. A., & Semmler, J. G. (2009) · Journal of Neurophysiology · doi:10.1152/jn.91285.2008 · Cited by 51
Prediction of Individual Finger Forces Based on Decoded Motoneuron Activities
Dai, C., Cao, Y., & Hu, X. (2019) · Annals of Biomedical Engineering · doi:10.1007/s10439-019-02240-1 · Cited by 46
Bräcklein, M., Ibáñez, J., Barsakcioglu, D. Y., Eden, J., Burdet, E., Mehring, C., & Farina, D. (2022) · eLife · doi:10.7554/elife.72871 · Cited by 44
Motor unit discharge rates of the anconeus muscle during high-velocity elbow extensions
Harwood, B. J., Davidson, A. W., & Rice, C. L. (2010) · Experimental Brain Research · doi:10.1007/s00221-010-2463-4 · Cited by 47
Ipsilesional deficits during fast diadochokinetic hand movements following unilateral brain damage
Hermsdörfer, J., & Goldenberg, G. (2002) · Neuropsychologia · doi:10.1016/s0028-3932(02)00048-9 · Cited by 45
Contractile function and motor unit firing rates of the human hamstrings
Kirk, E. A., & Rice, C. L. (2016) · Journal of Neurophysiology · doi:10.1152/jn.00620.2016 · Cited by 45
“For sampling of MUFRs during voluntary contractions, two intramuscular tungsten electrodes (Bellemare et al. 1983; Fuglevand et al. 1993) were inserted into the hamstring muscles, one into the lateral musculature (BF) and one into the medial (SS) musculature half the distance from the head of the fibula to the greater…”
MethodDai, C., & Hu, X. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.03.009 · Cited by 46
“In addition, a less degree of superposition during the ramp-up or ramp-down contractions can also help the separation of the different sources [23].”
BackgroundSafety and efficacy of strength training in patients with sporadic inclusion body myositis
Spector, S. A., Lemmer, J. T., Koffman, B. M., Fleisher, T. A., Feuerstein, I. M., Hurley, B. F., & Dalakas, M. C. (1997) · Muscle & Nerve · doi:10.1002/(sici)1097-4598(199710)20:10 · Cited by 155
Farina, D., Gazzoni, M., & Camelia, F. (2004) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01047.2003 · Cited by 45
“Indeed, at a given force level, the low-threshold MUs have higher firing rates than the high-threshold ones (15), which may affect the difference between CV values of MUs with different recruitment thresholds.”
BackgroundSynaptic integration in motoneurons with hyper-excitable dendrites
Heckman, C. J., Kuo, J. J., & Johnson, M. D. (2004) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/y04-046 · Cited by 44
“The basic pool – muscle input-output function is sigmoidal (Fuglevand et al. 1993; Heckman and Binder 1991), as shown in Fig.”
MethodWatanabe, R. N., Magalhães, F. H., Elias, L. A., Chaud, V. M., Mello, E. M., & Kohn, A. F. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00073.2013 · Cited by 44
“To test even further the phenomenological neuromuscular model mentioned above (Fuglevand et al. 1993), Keenan and Valero-Cuevas (2007) added to the original Fuglevand model Address for reprint requests and other correspondence: R.”
BackgroundKirk, E. A., Copithorne, D. B., Dalton, B. H., & Rice, C. L. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2016.05.059 · Cited by 44
“Doublets (>100 Hz) were excluded from analysis and a coefficient of variation <30% for the inter-spike intervals of each MU train were required for inclusion (Fuglevand et al., 1993).”
Dideriksen, J. L., & Farina, D. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006985 · Cited by 44
Vecchio, A. D., Falla, D., Felici, F., & Farina, D. (2019) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00139.2019 · Cited by 44
“The force exerted by each motor unit was modeled with previously described equations (18), and the distribution of the absolute forces was obtained from previous experimental data on tibialis anterior motor unit twitch forces (43).”
MethodGabriel, D. A., & Kamen, G. (2008) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2007.10.009 · Cited by 44
“where (a) denotes the range of threshold values over the total number of MUs (n), and RR is the recruitment range (Fuglevand et al., 1993).”
BackgroundInfluentialOptimized Wavelets for Blind Separation of Nonstationary Surface Myoelectric Signals
Farina, D., Lucas, M.-F., & Doncarli, C. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.897844 · Cited by 44
Dißelhorst-Klug, C., Silny, J., & Rau, G. (1998) · Journal of Electromyography and Kinesiology · doi:10.1016/s1050-6411(98)00015-7 · Cited by 44
“considers both signal generation and neuronal control at the same time, but this model is based on an unbounded volume conductor and neglects, in this way, the influence of the skin surface [44].”
BackgroundLimit cycle behavior in spasticity: analysis and evaluation
Rymer, W. Z., & Hidler, J. M. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.887937 · Cited by 44
The Use of a Bayesian Neural Network Model for Classification Tasks
Holst, A. G. (1997) · Cited by 44
A review of occupationally-relevant models of localised muscle fatigue
Rashedi, E., & Nussbaum, M. A. (2015) · International Journal of Human Factors Modelling and Simulation · doi:10.1504/ijhfms.2015.068119 · Cited by 43
“Fuglevand et al. (1993) developed a theoretical model of MU recruitment and rate coding that was used in several subsequent studies (Barry et al., 2007; Keenan and ValeroCuevas, 2007; Dideriksen et al., 2010).”
MethodResultModels of passive and active dendrite motoneuron pools and their differences in muscle force control
Elias, L. A., Chaud, V. M., & Kohn, A. F. (2012) · Journal of Computational Neuroscience · doi:10.1007/s10827-012-0398-4 · Cited by 43
“The MU twitch model was the discrete-time impulse response of a second-order critically-damped system (Fuglevand et al. 1993; Cisi and Kohn 2008), which was described by Equation (9), with Apeak the twitch amplitude (in N); tpeak the twitch contraction time (in ms); T the time step (in ms); and…”
MethodDideriksen, J., Muceli, S., Dosen, S., Laine, C. M., & Farina, D. (2015) · Journal of applied physiology · doi:10.1152/japplphysiol.00327.2014 · Cited by 36
“Exponential distributions across the MN population were applied (37) within these ranges of the parameters (19).”
MethodFast generation model of high density surface EMG signals in a cylindrical conductor volume
Carriou, V., Boudaoud, S., Laforêt, J., et al. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.04.019 · Cited by 43
Zheng, Y., & Hu, X. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721500106 · Cited by 43
Na, Y., Choi, C., Lee, H., et al. (2015) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2386856 · Cited by 42
“[21] investigated the quantitative relationships between SEMG and force using contractile property of MUs and electrical behaviors of neurons.”
BackgroundVariability of successive contractions subtracted from unfused tetanus of fast and slow motor units
Raikova, R., Pogrzebna, M., Drzymała‐Celichowska, H., et al. (2007) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2007.02.010 · Cited by 41
“Moreover, it is necessary when muscle models consisting of MUs are constructed (Fuglevand et al., 1993; Raikova and Aladjov, 2004).”
BackgroundMulder, E. R., Gerrits, K. H. L., Kleine, B. U., Rittweger, J., Felsenberg, D., HAAN, A. D., & Stegeman, D. F. (2007) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2007.04.002 · Cited by 41
“for higher forces, the EMG increases steeper than force (Fuglevand et al., 1993).”
BackgroundNeuronal network models of motor generation and control
Lansner, A., & Ekeberg, Ö. (1994) · Current Opinion in Neurobiology · doi:10.1016/0959-4388(94)90140-6 · Cited by 41
Signal-dependent wavelets for electromyogram classification
Maitrot, A., Lucas, M. F., Doncarli, C., & Farina, D. (2005) · Medical & Biological Engineering & Computing · doi:10.1007/bf02344730 · Cited by 40
Tremor‐related motor unit firing in Parkinson's disease: implications for tremor genesis
Christakos, C. N., Erimaki, S., Anagnostou, E., & Anastasopoulos, D. (2009) · The Journal of Physiology · doi:10.1113/jphysiol.2009.173989 · Cited by 40
Force variability is mostly not motor noise: Theoretical implications for motor control
Nagamori, A., Laine, C. M., Loeb, G. E., & Valero‐Cuevas, F. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008707 · Cited by 44
“Despite its original purpose for simulating the relationship between isometric muscle force and electromyogram (EMG) of muscle [30], the Fuglevand model has been repurposed to simulate force variability [9, 28, 31–41, 48].”
BackgroundMethodResultInfluentialSreenivasa, M., Ayusawa, K., & Nakamura, Y. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2478858 · Cited by 39
“Neural spike to activation was modeled as the impulse response of a second-order system [14].”
BackgroundMethodMuscle Force Estimation Based on Neural Drive Information From Individual Motor Units
Zhang, X., Zhu, G., Chen, M., Chen, X., Chen, X. '., & Zhou, P. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3042788 · Cited by 38
“In general, MUs are gradually recruited in order of size (i.e., the size principle [6]).”
BackgroundMethodInfluentialOn the reliability and validity of central fatigue determination
Dotan, R., Woods, S., & Contessa, P. (2021) · European Journal of Applied Physiology · doi:10.1007/s00421-021-04700-w · Cited by 39
“The reasons for this are several and include incomplete summation of individual MU twitches from a single elicited contraction (e.g., Bawa and Stein 1976; Fuglevand et al. 1993), force absorbed in overcoming the muscle’s series elastic component (Wilkie 1956), possible tendon slack (Muraoka et al.”
BackgroundBoe, S. G., Rice, C. L., & Doherty, T. J. (2008) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2007.09.047 · Cited by 39
Simulations of motor unit number estimation techniques
Major, L. A., & Jones, K. E. (2005) · Journal of Neural Engineering · doi:10.1088/1741-2560/2/2/003 · Cited by 39
Reflex muscle contractions can be elicited by valgus positional perturbations of the human knee
Dhaher, Y. Y., Tsoumanis, A. D., & Rymer, W. Z. (2003) · Journal of Biomechanics · doi:10.1016/s0021-9290(02)00334-2 · Cited by 39
“An unloading perturbation provides additional excitatory input to the motor pool and causes more motoneurons to fire (Fuglevand et al., 1993), resulting in increased EMG activity, above that observed in the loading and sustained phases of the stimulus.”
BackgroundMordhorst, M., Heidlauf, T., & Röhrle, O. (2015) · Interface Focus · doi:10.1098/rsfs.2014.0076 · Cited by 38
“Further, an exponential distribution of the innervation number was assumed, where the largest MU had 100 times as many fibres as the smallest MU [44].”
BackgroundMethodEffect of fatigue on force fluctuations in knee extensors in young adults
Singh, N. B., Arampatzis, A., Duda, G. N., Heller, M. O. W., & Taylor, W. R. (2010) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2010.0091 · Cited by 37
“Since an increase in force magnitude has been associated with an increase in muscle activation (Fuglevand et al. 1993; Hunter et al. 2003; Sogaard et al. 2006; Missenard et al. 2009) and a shift in spectral power towards higher frequency components (Slifkin & Newell 1999), it is likely that the…”
BackgroundInfluentialCallahan, D. M., Umberger, B. R., & Kent, J. A. (2016) · The Journal of Physiology · doi:10.1113/jp271400 · Cited by 38
“1, step 2), which closely follows a model by Fuglevand et al. (1993) and predicts a firing rate (FR) for each simulated neuron.”
BackgroundNeuromorphic meets neuromechanics, part I: the methodology and implementation
Niu, C. M., Jalaleddini, K., Sohn, W. J., Rocamora, J., Sanger, T. D., & Valero‐Cuevas, F. J. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa593c · Cited by 38
“This produced realistic surface EMG signals as described in previous studies [34].”
Vecchio, A. D., Casolo, A., Dideriksen, J. L., Aagaard, P., Felici, F., Falla, D., & Farina, D. (2021) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00218.2021 · Cited by 46
Neuromuscular Reflexes Contribute to Knee Stiffness During Valgus Loading
Dhaher, Y. Y., Tsoumanis, A. D., Houle, T. T., et al. (2004) · Journal of Neurophysiology · doi:10.1152/jn.00921.2004 · Cited by 38
Koo, T. K. K., & Mak, A. F. T. (2005) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2005.03.012 · Cited by 38
The medial gastrocnemius muscle attenuates force fluctuations during plantar flexion
Shinohara, M., Yoshitake, Y., Kouzaki, M., et al. (2005) · Experimental Brain Research · doi:10.1007/s00221-005-0119-6 · Cited by 38
Analysis of surface EMG spike shape across different levels of isometric force
Gabriel, D. A., Lester, S., Lenhardt, S. A., & Cambridge, E. D. J. (2006) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.07.004 · Cited by 38
The inverse dynamics problem of neuromuscular control
HATZE, H. (2000) · Biological Cybernetics · doi:10.1007/s004220050013 · Cited by 38
Watanabe, K., Holobar, A., Mita, Y., Kouzaki, M., Ogawa, M., Akima, H., & Moritani, T. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01733 · Cited by 37
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“(10), but the values were constrained between 1 and 120 mN·m.”
BackgroundMethodResultInfluentialSustained Cycling Exercise Increases Intracortical Inhibition
Sidhu, S. K., Lauber, B., Cresswell, A. G., & Carroll, T. J. (2013) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e31827b119c · Cited by 37
Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition
Na, Y., & Kim, J. W. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2628373 · Cited by 37
“Mechanical muscle behavior indicates the twitch force, which is a generated force in an MU by a single action potential [27].”
BackgroundNeuromorphic Model of Reflex for Realtime Human-Like Compliant Control of Prosthetic Hand
Niu, C. M., Luo, Q., Chou, C.-H., Liu, J., Hao, M., & Lan, N. (2020) · Annals of Biomedical Engineering · doi:10.1007/s10439-020-02596-9 · Cited by 36
Activities of human genioglossus motor units
Bailey, E. F. (2011) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2011.04.018 · Cited by 37
Caillet, A. H., Phillips, A., Farina, D., & Modenese, L. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010556 · Cited by 38
Motor unit firing rate patterns during voluntary muscle force generation: a simulation study
Hu, X., Rymer, W. Z., & Suresh, N. L. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/2/026015 · Cited by 36
Boe, S. G., Dalton, B. H., Harwood, B. J., et al. (2009) · Clinical Neurophysiology · doi:10.1016/j.clinph.2009.02.168 · Cited by 36
“…plot, based on visual inspection; (2) a physiological firing rate quantified by a Gaussian-shaped main peak of the interdischarge interval histogram with an associated coefficient of variation of less than 0.3 (Fuglevand et al., 1993; Stashuk, 1999); and (3) a minimum of 51 detected potentials.”
InfluentialDo Synergies Improve Accuracy? A Study of Speed-Accuracy Trade-Offs during Finger Force Production
Gorniak, S. L., Duarte, M., & Latash, M. L. (2008) · Motor Control · doi:10.1123/mcj.12.2.151 · Cited by 36
Cudicio, A., Martinez‐Valdes, E., Cogliati, M., Orizio, C., & Negro, F. (2021) · European Journal of Applied Physiology · doi:10.1007/s00421-021-04829-8 · Cited by 35
Salomoni, S. E., & Graven‐Nielsen, T. (2012) · European Journal of Applied Physiology · doi:10.1007/s00421-012-2343-7 · Cited by 35
“…very sensitive to changes in motor unit recruitment strategy and spatial reorganization of active motor units (particularly at low force levels) (Fuglevand et al. 1993; Taylor et al. 2003), which are commonly reported by experimental pain studies using intramuscular and multi-channel surface…”
BackgroundJesunathadas, M., Marmon, A. R., Gibb, J. M., & Enoka, R. M. (2010) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00807.2009 · Cited by 36
Extreme Levels of Noise Constitute a Key Neuromuscular Deficit in the Elderly
Singh, N. B., König, N., Arampatzis, A., Heller, M. O. W., & Taylor, W. R. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048449 · Cited by 34
“Fluctuations in force generation during submaximal isometric contractions within a particular muscle are known to be dependent upon the discharge properties of the motor units recruited to perform the required task [1,3].”
BackgroundEstimation of Muscle Fiber Conduction Velocity With a Spectral Multidip Approach
Farina, D., & Negro, F. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.892928 · Cited by 35
“All motor units were active with discharge rate uniformly distributed in the range 8–40 pulses/s (interpulse interval variability 20%), assigned so that the smaller motor units had higher discharge rate than the larger ones [12].”
BackgroundNeural and non-neural related properties in the spastic wrist flexors: An optimization study
Wang, R., Herman, P. A., Ekeberg, Ö., et al. (2017) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2017.06.023 · Cited by 35
Zheng, Y., & Hu, X. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3102947 · Cited by 35
“To synthesize EMG signals, we used a previously developed MU pool model [29] to first generate firing activities of individual MUs under a pseudorandom neural drive trajectory.”
MethodMotor unit activity after eccentric exercise and muscle damage in humans
Semmler, J. G. (2014) · Acta Physiologica · doi:10.1111/apha.12232 · Cited by 34
“This pattern of muscle activity is opposite to the expected increase in EMG with fatigue during submaximal contractions (Fuglevand et al. 1993b), due at least in part to the recruitment of additional motor units to compensate for the fatigue-related decline in the ability of the active muscle…”
BackgroundCarbonaro, M., Meiburger, K. M., Seoni, S., Hodson-Tole, E. F., Vieira, T., & Botter, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-12999-4 · Cited by 36
Knikou, M., Angeli, C., Ferreira, C. K., et al. (2009) · International Journal of Neuroscience · doi:10.1080/00207450903139747 · Cited by 34
Keenan, K. G., Farina, D., Meyer, F. G., et al. (2006) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00491.2006 · Cited by 34
“In previous versions of the model (20, 63), motor unit territories were restricted to be circular and within the muscle; thus the largest motor unit territories overlapped within the center of the muscle, resulting in high fiber densities.”
BackgroundMethodCoordination of digit force variability during dominant and non-dominant sustained precision pinch
Li, K., Wei, N., Yue, S., et al. (2015) · Experimental Brain Research · doi:10.1007/s00221-015-4276-y · Cited by 33
“Previous studies have shown that the unfused firing of motor units (Fuglevand et al. 1993) and the variability of the discharge rate (Moritz et al.”
Compensatory plasticity in diaphragm and intercostal muscle utilization in a rat model of ALS
Seven, Y. B., Nichols, N. L., Kelly, M. N., Hobson, O. R., Satriotomo, I., & Mitchell, G. S. (2017) · Experimental Neurology · doi:10.1016/j.expneurol.2017.10.015 · Cited by 32
“RMS EMG value is a measure of the power of a signal and well correlated with force generated by various muscles including diaphragm muscle (Fuglevand et al., 1993; Lawrence and De Luca, 1983; Mantilla et al., 2010).”
BackgroundKirk, E. A., Christie, A. D., Knight, C. A., & Rice, C. L. (2021) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01047.2020 · Cited by 33
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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