The DOI matches its Crossref record.
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)
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
Cited by
Sorted by citations
Items 501–600 of 983
Dideriksen, J. L., Feeney, D. F., Almuklass, A. M., & Enoka, R. M. (2017) · Experimental Brain Research · doi:10.1007/s00221-017-4995-3 · Cited by 12
“The approach adopted a previously published computational model that established the recruitment and rate coding characteristics in a population of motor neurons and the isometric force generated from the motor neuron activation (Fuglevand et al. 1993; Barry et al. 2007).”
Kirk, E. A., Gilmore, K. J., & Rice, C. L. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00219.2021 · Cited by 12
Xu, Y., Yu, Y., Zhao, Z., Chen, C., & Sheng, X. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3309662 · Cited by 12
Gogeascoechea, A., Ornelas-Kobayashi, R., Yavuz, U. Ş., & Sartori, M. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3319959 · Cited by 13
“Each twitch response was computed as an impulse response of a second-order system (4) [17]: where a j is the activation of the j th MU, A c the peak amplitude, T c the contraction time and u the excitation input, i.e. , a MU spike train.”
Methodde Bruijn, E., Nijmeijer, S. W. R., Forbes, P. A., et al. (2015) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2015.03.047 · Cited by 12
“This was interpreted to point towards a change in central command strategy to deliver the required force [19].”
BackgroundAn integrated model of the neuromuscular system
Heidlauf, T., Negro, F., Farina, D., et al. (2013) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner.2013.6695913 · Cited by 12
Rate modulation of human anconeus motor units during high-intensity dynamic elbow extensions
Cowling, B. L., Harwood, B. J., Copithorne, D. B., et al. (2016) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00131.2016 · Cited by 12
Carriou, V., Boudaoud, S., Laforêt, J., et al. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103480 · Cited by 12
“Each MU is activated at a specific threshold depicted in the Fuglevand model [15].”
BackgroundHuang, C., Chen, M., Zhang, Y., et al. (2021) · Neural Plasticity · doi:10.1155/2021/5513224 · Cited by 12
“As stated in the original model [15], the greatest gain in motor unit force occurs when the ISI is equivalent to the twitch contraction time.”
BackgroundMethodInfluentialRaikova, R., Rusev, R., Drzymała‐Celichowska, H., et al. (2010) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2010.01.034 · Cited by 12
“, 1995) or a gain in MU force has been provided for higher firing rate frequencies (Fuglevand et al., 1993).”
BackgroundInfluentialNegro, F., Yavuz, U. Ş., & Farina, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0092390 · Cited by 12
Supraspinal control of motoneurons after paralysis enabled by spinal cord stimulationPreprint
Balaguer, J.-M., Prat-Ortega, G., Verma, N., Yadav, P., Sorensen, E., de Freitas, R., Ensel, S., Borda, L., Donadio, S., Liang, L., et al. (2023) · medRxiv · doi:10.1101/2023.11.29.23298779 · Cited by 13
“To estimate the force from the motoneuron pool firing rate, we used the model proposed in 30 .”
MethodImmediate Effects of Acupuncture on Explosive Force Production and Stiffness in Male Knee Joint
Wang, J., Wang, I., Hu, R., Yao, S., Su, Y., Zhou, S. M., & Chen, C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18189518 · Cited by 10
“The increase in the pre-activation level provides extra excitatory input for the motor pool and leads to more motor neuron activation [47].”
BackgroundMa, S., Chen, C., Zhao, J., et al. (2021) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2021.3112766 · Cited by 11
Saini, H., Klotz, T., & Röhrle, O. (2022) · Biomechanics and Modeling in Mechanobiology · doi:10.1007/s10237-022-01666-2 · Cited by 11
Ullah, K. (2011) · Journal of Engineering and Technology Research · doi:10.5897/jetr11.067 · Cited by 11
“In one of these models torque is calculated on basis of response from a single motor (Fuglevand et al., 1993). Herbert and Gandevia (1999) improved this system by providing an accurate single motor response. Statistical optimization methods are also used by researcher to extract the joint torque and muscle tension fro…”
BackgroundInfluentialShin, H., Suresh, N. L., Rymer, W. Z., et al. (2017) · Journal of Neural Engineering · doi:10.1088/1741-2552/aa925d · Cited by 11
Coker, R., Zellmer, E. R., & Moran, D. W. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaefcf · Cited by 11
Contributions to muscle force and EMG by combined neural excitation and electrical stimulation
Crago, P. E., Makowski, N. S., & Cole, N. M. (2014) · Journal of Neural Engineering · doi:10.1088/1741-2560/11/5/056022 · Cited by 11
“Thus, 10% MVC increments for the De Luca and Contessa model are approximately 5% MVC for the Fuglevand et al model.”
MethodResultInfluentialLepora, N. F., Porrill, J., Yeo, C. H., et al. (2009) · Journal of Neurophysiology · doi:10.1152/jn.00204.2009 · Cited by 11
“In accordance with previous studies (Fuglevand et al. 1993; Keenan and Valero-Cuevas 2007; Zhou and Rymer 2004) the gains G i were assumed to be the same (ϭG) for all motoneurons (cf. properties of abducens motoneurons as discussed in Dean 1997) and the thresholds R i varied, to give many…”
On the impact of spike segmentation on motor unit identification in dynamic surface electromyograms
Glaser, V., & Holobar, A. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8036854 · Cited by 11
“MU recruitment was based on the model proposed in [2].”
MethodSpeedup computation of HD-sEMG signals using a motor unit-specific electrical source model
Carriou, V., Boudaoud, S., & Laforêt, J. (2018) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-018-1784-5 · Cited by 11
Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms
Holobar, A., Gallego, J. Á., Kranjec, J., Rocón, E., Romero, J. P., Benito‐León, J., Pons, J. L., & Glaser, V. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00879 · Cited by 11
“Finally, the firing variability 1drk of each individual motor unit tr (n) was modeled as Gaussian random variable 1drk ∼ N(0, σ1dr ) (33, 34) (see Appendix).”
MethodMathematical analysis of a muscle architecture model
Navallas, J., Malanda, A., Gila, L., et al. (2008) · Mathematical Biosciences · doi:10.1016/j.mbs.2008.10.004 · Cited by 11
Physiology of Muscle Activation and Force Generation
Enoka, R. M., & Duchateau, J. (2016) · Wiley eBooks · doi:10.1002/9781119082934.ch01 · Cited by 12
Nishikawa, T., Takeda, R., Hirono, T., Okudaira, M., Ohya, T., & Watanabe, K. (2023) · Experimental Gerontology · doi:10.1016/j.exger.2023.112346 · Cited by 10
Pulse Shape and Voltage-Dependent Synchronization in Spiking Neuron Networks
Pietras, B. (2024) · Neural Computation · doi:10.1162/neco_a_01680 · Cited by 12
Liu, Y., Zhang, X., Zhao, H., Chen, X. '., & Yao, B. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3477607 · Cited by 10
“A common approach is to employ mathematical models to simulate the MU contractile twitch characteristics, and many resultant studies have been implemented [43], [44], [45], [46].”
MethodA motor unit action potential-based method for surface electromyography decomposition
Chen, C., Li, D., & Xia, M. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01595-y · Cited by 10
“The discharge rate then increased by 0.3 Hz per percent of excitation until reaching a maximum frequency of 35 Hz [31].”
InfluentialSohn, W. J., Niu, C. M., & Sanger, T. D. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/036010 · Cited by 10
Fiogbé, E., Carnavale, B. F., & Takahashi, A. C. D. M. (2019) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2019.04.005 · Cited by 11
Raikova, R., Krasteva, V., Krutki, P., Drzymała‐Celichowska, H., Kryściak, K., & Celichowski, J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008282 · Cited by 10
Rohlén, R., Antfolk, C., & Grönlund, C. (2022) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2022.102714 · Cited by 10
A computational model of surface electromyography signal alterations after spinal cord injury
Li, G., Balbinot, G. C., Furlan, J. C., Kalsi‐Ryan, S., & Zariffa, J. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad0b8e · Cited by 10
“Under steady-state excitation, the ISI are modeled as normally distributed [26, 38].”
BackgroundMethodInfluentialJiang, M. C., Elbasiouny, S. M., Collins, W. F., & Heckman, C. J. (2015) · Journal of Neurophysiology · doi:10.1152/jn.00337.2015 · Cited by 10
Guo, F., Wang, J., Sun, Y.-J., Yang, A., & Zhang, R.-H. (2014) · NeuroReport · doi:10.1097/wnr.0000000000000242 · Cited by 7
Elias, L. A., Matoso, D. E. D. C., Watanabe, R. N., et al. (2018) · Research on Biomedical Engineering · doi:10.1590/2446-4740.00118 · Cited by 10
“These models are preferable to simple twitch representations (Fuglevand et al., 1993) since variations in muscle length and tendon tension are used as feedback variables in most scenarios (see the blue rectangles in Figure 1B).”
BackgroundMethodHolobar, A., & Glaser, V. (2018) · Biosystems & biorobotics · doi:10.1007/978-3-030-01845-0_228 · Cited by 10
Watanabe, K., Holobar, A., Uchida, K., & Mita, Y. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.645747 · Cited by 10
“Median firing rates with >30% coefficient of variation were excluded for further analysis (29).”
BackgroundHirono, T., Kunugi, S., Yoshimura, A., Ueda, S., Goto, R., Akatsu, H., & Watanabe, K. (2023) · Aging Clinical and Experimental Research · doi:10.1007/s40520-023-02370-6 · Cited by 11
Lubel, E., Sgambato, B. G., Rohlén, R., et al. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3315146 · Cited by 10
System identification of a motor unit pool using a realistic neuromusculoskeletal model
Watanabe, R. N., & Kohn, A. F. (2014) · 2022 9th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob) · doi:10.1109/biorob.2014.6913845 · Cited by 10
“Each spike generated by a MN produces a twitch in the muscle unit following the impulse response of a second-order critically-damped system [4], [9].”
BackgroundRaikova, R., Krutki, P., Aladjov, H., et al. (2007) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2007.02.006 · Cited by 10
“In models that investigate the control of a muscle composed from many MUs [4,5,21] it is usually accepted that S MUs are weaker than FR and FF MUs.”
BackgroundVenugopal, G., Deepak, P., Ghosh, D. M., & Ramakrishnan, S. (2017) · Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine · doi:10.1177/0954411917727307 · Cited by 10
“A proportional–integral–derivative (PID) controller is used to track the error in force and to estimate the required excitatory drive to the motor neuron pool for a given force profile (required excitatory input (REI)).28 The error in force, f(t), corresponds to the difference of the predefined input force (Ft) with t…”
BackgroundSeparation of interference surface electromyogram into propagating and non-propagating components
Mesin, L. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.04.016 · Cited by 12
Influence of age on motor control accuracy during static ramp contractions
Cogliati, M., Cudicio, A., Negro, F., et al. (2019) · Experimental Brain Research · doi:10.1007/s00221-019-05524-z · Cited by 10
Effect of number of motor units and muscle fibre type on surface electromyogram
Arjunan, S. P., Kumar, D., Wheeler, K. A., Shimada, H., & Siddiqi, A. (2015) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-015-1344-1 · Cited by 10
“There have been significant improvements in sEMG signal models and their implementations [3, 7, 10, 17, 21] where factors such as the dimensions of the muscles, volume conductance effects, motor unit size, muscle fibre length and recruitment thresholds have been considered.”
BackgroundSurface EMG pre-processing techniques for the detection of common input to motor neuron populations
Istenič, R., Negro, F., Holobar, A., et al. (2013) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2013.6746944 · Cited by 10
“Higher-order Activity index performed better than original Activity index, coherence values were in the range of rectified EMG. Keywords-surface EMG; motor neuron model; activity index; EMG-EMG coherence I. INTRODUCTION EMG-EMG coherence is a common tool used to investigate common neural pathways…”
MethodComparative evaluation of motor unit architecture models
Navallas, J., Malanda, A., Gila, L., et al. (2009) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-009-0526-0 · Cited by 10
Legreneur, P., Morlon, B., & Hoecke, J. V. (1997) · Archives of Physiology and Biochemistry · doi:10.1076/apab.105.5.450.3286 · Cited by 10
Holobar, A., Farina, D., & Zazula, D. (2016) · doi:10.1002/9781119082934.ch07 · Cited by 10
Dideriksen, J. L., Elias, L. A., Zambalde, E. P., et al. (2021) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2021.110866 · Cited by 9
Caillet, A. H., Phillips, A., Modenese, L., & Farina, D. (2024) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2024.102873 · Cited by 9
EMG coherence of foot and ankle muscles increases with a postural challenge in men
Tsiouri, C., Amiridis, I. G., Kannas, T., et al. (2024) · Gait & Posture · doi:10.1016/j.gaitpost.2024.06.019 · Cited by 9
Motor unit firing statistics and the Fuglevand model
Jones, K. E. (2005) · Journal of Neurophysiology · doi:10.1152/jn.00430.2005 · Cited by 9
PyMUS: Python-Based Simulation Software for Virtual Experiments on Motor Unit System
Kim, H., & Kim, M. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00015 · Cited by 9
“The static motoneuron models have been used to represent the input–output relationship of motoneurons at steady state (Heckman and Binder, 1991, 1993; Fuglevand et al., 1993).”
MethodA Lower Limb Phantom for Simulation and Assessment of Electromyography Technology
Schlink, B. R., & Ferris, D. P. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2944297 · Cited by 9
“We transmitted two types of files to the phantom throughout the different phases of the experiment: (1) sinusoidal bursts generated using software (MATLAB, The Mathworks, Natick, MA) and (2) simulated EMG data created using an established model [33], [34].”
MethodNell, H. J., Castelli, L. M., Bertani, D., Jipson, A. A., Meagher, S. F., Melo, L. T. M., Zabjek, K., & Reid, W. D. (2020) · BMC Sports Science Medicine and Rehabilitation · doi:10.1186/s13102-020-00163-2 · Cited by 9
Zhang, X., Tang, X. L., Wei, Z., et al. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.3002792 · Cited by 9
“1) Model Description: The motor neuron pool model transfers excitation into MU recruitment and repetitive firings, and the source of synaptic input was assumed as being uniform distribution across the population of motoneurons [18].”
BackgroundMethodUrh, F., Strnad, D., Clarke, A., Farina, D., & Holobar, A. (2020) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc44109.2020.9176294 · Cited by 10
“Firing pattern of each MU was generated with the model proposed in [20].”
MethodInverse modelling to reduce crosstalk in high density surface electromyogram
Mesin, L. (2020) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2020.09.011 · Cited by 9
Frančič, A., & 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_103 · Cited by 9
Zero, A. M., Kirk, E. A., Hali, K., & Rice, C. L. (2021) · Neuroscience Letters · doi:10.1016/j.neulet.2021.136118 · Cited by 9
Motor unit firing patterns on increasing force during force and position tasks
Kunugi, S., Holobar, A., Kodera, T., Toyoda, H., & Watanabe, K. (2021) · Journal of Neurophysiology · doi:10.1152/jn.00299.2021 · Cited by 9
Prakash, S. R., Herrmann, B. S., Milojcic, R., et al. (2015) · Ear and Hearing · doi:10.1097/aud.0000000000000158 · Cited by 9
“The fiber counts in the motor units were exponentially distributed (Fuglevand et al. 1993).”
BackgroundSpike shape analysis for the surface and needle electromyographic interference pattern
Green, L. A., Christie, A. D., & Gabriel, D. A. (2017) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2017.03.006 · Cited by 9
Dideriksen, J. L., & Negro, F. (2018) · Journal of Electromyography and Kinesiology · doi:10.1016/j.jelekin.2018.09.008 · Cited by 9
“The study adopted the model of motor unit activity and isometric force generation developed by (Fuglevand et al., 1993) with the distribution of minimum and peak discharge rates proposed by (Barry et al.”
MethodInfluentialKranjec, J., & Holobar, A. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2922453 · Cited by 10
“MU recruitment and spike trains were generated by the model proposed in [6], with parameters adapted to the values reported for the biceps brachii muscle.”
MethodEMG Based Muscle Force Estimation using Motor Unit Twitch Model and Convolution Kernel Compensation
Istenič, R., Holobar, A., Merletti, R., et al. (2007) · Springer eBooks · doi:10.1007/978-3-540-73044-6_29 · Cited by 9
“As we know that the force produced by a muscle means a resultant of all MU forces, we also have to take into account that the MUs trigger with different frequencies, they may be recruited at different time intervals, and the amount of force they exert at every excitation (the force twitch) depends on the MU type [2].”
BackgroundMethodInfluentialFrank, T. D., Patanarapeelert, K., & Beek, P. J. (2008) · Physics Letters A · doi:10.1016/j.physleta.2008.02.031 · Cited by 9
Modified motor unit properties in residual muscle following transtibial amputation
Rubin, N., Hinson, R. M., Saul, K. R., Filer, W. G., Hu, X., & Huang, H. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad1ac2 · Cited by 8
“If excitatory drive and firing rates for MUs were not impacted by amputation, and RTs of MUs reversed, correlations between the RT and MFR would reduce.”
BackgroundMethodInfluentialAdaptive Neural Decoder for Prosthetic Hand Control
Montgomery, A. E., Allen, J. M., & Elbasiouny, S. M. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.590775 · Cited by 8
Kalc, M., Škarabot, J., Divjak, M., Urh, F., Kramberger, M., Vogrin, M., & Holobar, A. (2022) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2022.3217450 · Cited by 8
“in [28], with the simulator parameters adapted to biceps brachii muscle.”
BackgroundQuadriceps torque complexity before and after anterior cruciate ligament reconstruction
Johnson, A. K., Rodriguez, K. M., Lepley, A. S., et al. (2023) · Journal of science and medicine in sport · doi:10.1016/j.jsams.2023.09.009 · Cited by 9
Nishikawa, T., Hirono, T., Holobar, A., Kunugi, S., Okudaira, M., Ohya, T., & Watanabe, K. (2024) · European Journal of Applied Physiology · doi:10.1007/s00421-023-05376-0 · Cited by 12
“Motor unit firing rates with > 30% coefficient of variation were excluded from further analysis (Fuglevand et al. 1993).”
Finite element model for the simulation of skeletal muscle fatigue
Böl, M., Pipetz, A., & Reese, S. (2009) · Materialwissenschaft und Werkstofftechnik · doi:10.1002/mawe.200800372 · Cited by 8
Mesin, L. (2013) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2013.03.007 · Cited by 8
“MUs were recruited according to the recruitment threshold excitation function proposed in [40] with range of recruitment thresholds equal to 75%.”
MethodMesin, L. (2014) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2014.11.011 · Cited by 8
Modifying motor unit territory placement in the Fuglevand model
Robertson, J. W., & Johnston, J. A. (2017) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-017-1645-7 · Cited by 8
“For our implementation, a mean density of 20 fibres/mm(2) [16] and a coefficient of variation of 0.”
BackgroundMethodResultInfluentialA dynamic calcium-force relationship model for sag behavior in fast skeletal muscle
Kim, H., & Heckman, C. J. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011178 · Cited by 7
“The computational model for fast skeletal muscle has been developed phenomenologically, focusing on capturing force-length and force-velocity properties across different levels of muscle excitation [55] or superposition of twitch responses under isometric conditions [56,57].”
MethodMultivariable closed-loop control of deep brain stimulation for Parkinson’s disease
Fleming, J. E., Senneff, S., & Lowery, M. M. (2023) · Journal of Neural Engineering · doi:10.1088/1741-2552/acfbfa · Cited by 8
“The force produced by each motoneuron was simulated using the force model introduced by (Fuglevand et al 1993), whereby motor unit twitch force was simulated as the impulse response of a critically damped, second-order system, incorporating nonlinearity of the force-firing rate relationship.”
Extracting Neural Drives from Surface EMG: A Generative Model and Simulation Studies
Jiang, N., Parker, P. A., & Englehart, K. B. (2007) · Conference proceedings · doi:10.1109/iembs.2007.4353423 · Cited by 8
“The motor unit pool of the ith muscle is driven by xi(t), and the motor units within the pool are recruited orderly and change their fire rates in concert (rate coding) [13].”
BackgroundInfluentialNeck inspiratory muscle activation patterns during well-controlled inspiration
Washino, S., Kanehisa, H., & Yoshitake, Y. (2017) · European Journal of Applied Physiology · doi:10.1007/s00421-017-3699-5 · Cited by 8
Miller, R. H. (2017) · Springer eBooks · doi:10.1007/978-3-319-30808-1_203-1 · Cited by 8
Miralles, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aaae19 · Cited by 8
“The standard deviation of the inter-spike interval was fixed for all motor units at 15 % of the mean interspike interval [22, 23].”
BackgroundMethodInfluentialDealing with time-varying recruitment and length in Hill-type muscle models
Hamouda, A., Kenney, L. P. J., & Howard, D. (2016) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2016.08.030 · Cited by 8
“In some cases, the motivation has been to create more complex physiologically based models that are capable of simulating the summation of individual motor-unit twitches (Riener and Quintern, 1997; Fuglevand et al., 1993).”
BackgroundConvolutive blind source separation of surface EMG measurements of the respiratory muscles
Petersen, E., Buchner, H., Eger, M., et al. (2017) · Biomedizinische Technik/Biomedical Engineering · doi:10.1515/bmt-2016-0092 · Cited by 8
“This MU distribution strategy results in overlapping MU areas, which is a desirable property of models for MU geometry [15].”
BackgroundAnalytical generation model of surface electromyogram for multi-layer volume conductors
Mesin, L. (2005) · WIT transactions on biomedicine and health · doi:10.2495/bio050101 · Cited by 8
Guerra, I. M., Barsakcioglu, D. Y., & Farina, D. (2024) · Journal of Neural Engineering · doi:10.1088/1741-2552/ad5ebf · Cited by 7
“Similarly, the aforementioned DR and CoV ranges correspond to the physiological behaviour of MN firings [24, 37] and their accurate identification [38].”
InfluentialA VMD-Based Noise-Specific and Adaptive Removal Method for High-Density Surface EMG
Wang, W., Gu, Y. X., Zhang, Y., Bai, J., & Liu, T. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3469387 · Cited by 8
“The recruitment and discharge strategies of the motoneuron pool are consistent with [34], where the recruitment thresholds of MUs are distributed exponentially and the excitation-firing rate relationship for MUs is linear.”
Association between physical fitness tests and neuromuscular properties
Hirono, T., Okudaira, M., Takeda, R., Ueda, S., Nishikawa, T., Igawa, K., Kunugi, S., Yoshimura, A., & Watanabe, K. (2024) · European Journal of Applied Physiology · doi:10.1007/s00421-023-05394-y · Cited by 7
“Any physiologically irregular MU discharge (less than 4 and over 50 Hz) and individual MUs with firing rates showing a coefficient of variation of over 30% were discarded (Adam and De Luca 2005; Welsh et al. 2007; Kirk et al. 2021; Kamen and Knight 2004; Fuglevand et al. 1993; Hirono et al. 2022b).”
Saxena, S., Sarma, S. V., & Dahleh, M. A. (2020) · Neural Computation · doi:10.1162/neco_a_01272 · Cited by 6
Subject-Specific EMG Modeling with Multiple Muscles: A Preliminary Study
Ma, S., Chen, C., Han, D., et al. (2020) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc44109.2020.9175286 · Cited by 7
“2) Motor unit recruitment pattern: The recruitment thresholds of motor neurons of each muscle were distributed in consistent with the principle described in [22].”
MethodLai, A. K. M., Dick, T. J. M., Biewener, A. A., & Wakeling, J. M. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2020.0765 · Cited by 8
“For this study, we clustered the muscle into two distinct slower and faster muscle fibre types, whereas, in reality, muscles consist of muscle fibres with physiological properties that span a continuum between the slowest and the fastest muscle fibre [31].”
MethodHuang, C., Chen, M., Li, X., et al. (2021) · Biosensors · doi:10.3390/bios11100356 · Cited by 7
Volk, V. L., Hamilton, L. D., Hume, D. R., Shelburne, K. B., & Fitzpatrick, C. K. (2021) · Scientific Reports · doi:10.1038/s41598-021-02298-9 · Cited by 7
Identification of Motor Unit Twitch Properties in the Intact Human In Vivo
Gogeascoechea, A., Ornelas-Kobayashi, R., Yavuz, U. Ş., et al. (2021) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc46164.2021.9630328 · Cited by 7
“This provides a faster twitch decay than the natural decay of Fuglevand’s model [14], which is in line with experimental observations [16], [17].”
ResultMScanFit motor unit number estimation of human anconeus muscle
Zong, Y., Lu, Z., Chen, M., et al. (2022) · Muscle & Nerve · doi:10.1002/mus.27487 · Cited by 7
Carbonaro, M., Zaccardi, S., Seoni, S., et al. (2022) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc48229.2022.9871578 · Cited by 7
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
