Force Distribution Model of Motor Units of Leg Extensor Muscles

The paper aims to defme the lawfulness （model）, by which one can reliably estimate the distribution of motor units＇ force from the moment when leg extensor muscles start to generate the force until the moment when the maximal level is achieved. The study included 110 participants. To assess the contractile characteristics of leg extensors, standardized equipment and standardized isometric test in sitting position with the angle of the knee joint of 125° were used. The participants were instructed to exert their maximal force as quickly as possible. Using descriptive statistics, cluster analysis and fitting the exponential model of the distribution of force of leg extensor motor units in the whole range of force generation is defined. The model has the following form： y = 0.2051e3.3855x, where y is the motor units force expressed in daN （decanewtons）, x is the time expressed in s （seconds）. It provides an understanding of the control of multivariate motor unit recruitment and distribution of their force during sports movements as well as training programming for the adoption of forms for conlrolling force distribution of motor units, the development of their maximum force and their involvement speed.

Towards semi-supervised myoelectric finger motion recognition based on spatial motor units activation

It is vital to recognize the intention of finger motions for human-machine interaction(HMI).The latest research focuses on fine myoelectric control through the decoding of neural motor unit action potential trains(MUAPt) from high-density surface electromyographic(sEMG) signals.However,the existing EMG decoding algorithms rarely obtain the spatial matching relationship between decoded motion units(MU) and designated muscles,and the control interface can only recognize the trained hand gestures.In this study,a semi-supervised HMI based on MU-muscle matching(MMM) is proposed to recognize individual finger motions and even the untrained combined multi-finger actions.Through automatic channel selection from high-density s EMG signals,the optimal spatial positions to monitor the MU activation of finger muscles are determined.Finger tapping experiment is carried out on ten subjects,and the experimental results show that the proposed s EMG decomposition algorithm based on MMM can accurately identify single finger motions with an accuracy of 93.1%±1.4%,which is comparable to that of state-of-the-art pattern recognition methods.Furthermore,the MMM allows unsupervised recognizing the untrained combined multi-finger motions with an accuracy of 73%±3.8%.The outcomes of this study benefit the practical applications of HMI,such as controlling prosthetic hand and virtual keyboard.

Electromyographic evaluation of functional electrical stimulation to injured oculomotor nerve

Functional electrical stimulation delivered early after injury to the proximal nerve stump has been proposed as a therapeutic approach for enhancing the speed and specificity of axonal regeneration following nerve injury. In this study, the injured oculomotor nerve was stimulated functionally by an implantable electrode. Electromyographic monitoring of the motor unit potential of the inferior oblique muscle was conducted for 12 weeks in two injury groups, one with and one without electric stimulation. The results revealed that, at 2, 4, 6, 8 weeks after functional electric stimulation of the injured oculomotor nerve, motor unit potentials significantly increased, such that amplitude was longer and spike duration gradually shortened. These findings indicate that the injured oculomotor nerve has the potential for regeneration and repair, but this ability is not sufficient for full functional recovery to occur. Importantly, the current results indicated that recovery and regeneration of the injured oculomotor nerve can be promoted with functional electrical stimulation.

Proximal-distal motor unit number estimationdynamic changes in patients with amyotrophic lateral sclerosis A one-year follow-up

BACKGROUND：Amyotrophic lateral sclerosis （ALS） is the most common of all the motor neuron diseases and the absence of a biologic marker has made both diagnosis and tracking evolution of the disease difficult, Electrodiagnostic tests play a fundamental role in quantifying pathological changes in the motor unit pool.OBJECTIVE：We assessed distal-proximal Motor Unit （MU） loss and changes using the method of motor unit number estimation （MUNE）.DESIGN, TIME AND SETTING：A case-control study was performed at the Department of Neuroscience, Pisa University Medical School, Italy from December 1999 to November 2009. PARTICIPANTS：A total of 50 ALS patients were recruited, 30 males：mean age （59.6 ± 13.3） years; 20 females：mean age （63.9 ± 11.7） years; range （30-82） years; all patients had probable or definite ALS. Thirty healthy volunteers were recruited from department staffs, including 20 males and 10 females; mean age （57.7 ± 13.8） years served as controls.METHODS：MUNE was performed for both the biceps brachii and abductor digiti minimi muscles of the same side. The technique used relayed substantially on manual incremental stimulation of the motor nerve, known as the McComas technique （50 ms sweep duration, a gain of 2 mV/Div for M wave, 0.5 mV/Div for each step; filters 10-20 kHz）.MAIN OUTCOME MEASURES：MUNE results were measured.RESULTS：Functioning MU numbers, measured by MUNE, decreased in the biceps brachii and abductor digiti minimi muscles over the entire one-year follow-up period （one assessment every three months） compared with baseline determination, the rate of MU decrease was similar in both muscles, but steeper distally.CONCLUSION：MUNE is a feasible method for ALS patients both proximally and distally to track changes over time in muscle MUs during the disease＇s evolution.

Compound muscle action potential(CMAP)scan examination of paretic and contralateral muscles reveals motor unit alterations after stroke

This study presents a novel compound muscle action potential(CMAP)examination of motor unit changes in paretic muscle post stroke.CMAP scan of the first dorsal interosseous(FDI)muscle was performed bilaterally in 16 chronic stroke subjects.Various parameters were derived from the CMAP scan to examine paretic muscle changes,including CMAP amplitude,D50,step index(STEPIX)and amplitude index(AMPIX).A significant decrease in CMAP amplitude and STEPIX was observed in paretic muscles compared with contralateral muscles(CMAP amplitude:paretic(9.0±0.5)mV,contralateral(11.3±0.9)mV,P=0.024;STEPIX:paretic 101.2±7.6,contralateral 121.9±6.5,P=0.020).No significant difference in D50 and AMPIX was observed between the paretic and contralateral sides(P>0.05).The findings revealed complex paretic muscle changes including motor unit degeneration,muscle fiber denervation,reinnervation and atrophy,providing useful insights to help understand neuromuscular mechanisms associated with weakness and other functional deterioration post stroke.The CMAP scan experimental protocols and the applied processing methods are noninvasive,convenient,and automated,offering practical benefits for clinical application.

A Study on sEMG Simulation Modeling and Its Decomposition Methods

In this paper, we establish a surface electromyography(sEMG) signal model and study the signal decomposition method from noisy background. Firstly, single fiber action potential (SFAP), motor unit action potential (MUAP) and motor unit action potential train(MUAPT) are simulated based on the tripolar signal source model, and then the sEMG is obtained; secondly, the simulated sEMG signal is extracted from the mixed signals that consists of white noises, power frequency interference signal and electrocardio signal by independent component analysis (ICA) algorithms; lastly, the spikes corresponding to each motor unit action potential from the simulated sEMG signals were detected by applying the wavelet transform (WT) method. Simulation results showed that sEMG model could describe the physiological process of sEMG, ICA and WT methods could extract the sEMG signal and its features, which will lay a foundation for further classifying the MUAP.

The Change of Spectral Energy Distribution of Surface EMG Signal During Forearm Action Process

Spectral energy distribution of surface EMG signal is often used but difficultly and effectively control artificial limb, because the spectral energy distribution changes in the process of limb actions. In this paper, the general characteristics of surface EMG signal patterns were firstly characterized by spectral energy change. 13 healthy subjects were instructed to execute forearm supination （FS） and forearm pronation （FP） with their right foreanns when their forearm muscles were ＂fatigue＂ or ＂relaxed＂. All surface EMG signals were recorded from their right forearm flexor during their right forearm actions. Two sets of surface EMG signals were segmented from every surface EMG signal appropriately at preparing stage and acting stage. Relative wavelet packet energy （symbolized by pnp and pna respectively at preparing stage and acting stage, n denotes the nth frequency band） of surface EMG signal firstly was calculated and then, the difference （Pn = Pna-Pnp） were gained. The results showed that Pn from some frequency bands can effectively characterize the general characteristics of surface EMG signal patterns. Compared with Pn in other frequency bands, P4, the spectral energy change from 93.75 to 125 Hz, was more appropriately regarded as the features.

A simulation study on the relation between muscle motor unit numbers and the non-Gaussianity/non-linearity levels of surface electromyography

Recent research has demonstrated that surface electromyography （sEMG） signals have non-Gaussianity and non-linearity properties. It is known that more muscle motor units are recruited and firing rates （FRs） increase as exertion increases. A hy- pothesis was proposed that the Gaussianity test （Sg） and linearity test （St） levels of sEMG signals are associated with the num- ber of active motor units （nMUs） and the FR. The hypothesis has only been preliminarily discussed in experimental studies. We used a simulation sEMG model involving spatial （active MUs） and temporal （three FRs） information to test the hypothesis. Higher-order statistics （HOS） from the bi-frequency domain were used to perform Sg and St. Multivariate covariance analysis and a correlation test were employed to determine the nMUs-Sg relationship and the nMUs-St relationship. Results showed that nMUs, the FR, and the interaction of nMUs and the FR all influenced the Sg and St values. The nMUs negatively correlated to both the Sg and St values. That is, at the three FRs, sEMG signals tended to a more Gaussian and linear distribution as exertion and nMUs increased. The study limited experiment factors to the sEMG non-Gaussianity and non-linearity levels. The study quantitatively described nMUs and the FR of muscle that are not directly available from experiments. Our finding has guiding significance for muscle capability assessment and prosthetic control.

Effect of repeated eccentric exercise on muscle damage markers and motor unit control strategies in arm and hand muscle

To examine the contralateral repeated bout effect(CL-RBE)on muscle damage markers and motor unit(MU)control strategies,seventeen healthy adults performed two bouts of 60 eccentric contractions with elbow flexor(EF group;n=9)or index finger abductor(IA group;n=8)muscles,separated by 1 week.All participants randomly performed eccentric exercise on either the right or left arm or hand muscles,and muscle damage markers and submaximal trapezoid contraction tests were conducted pre,post,1-and 2-day post eccentric protocol.One week after the first bout,the same exercise protocol and measurements were performed on the contralateral muscles.Surface electromyographic(EMG)signals were collected from biceps brachii(BB)or first dorsal interosseous(FDI)during maximal and submaximal tests.The linear regression analyses were used to examine MU recruitment threshold versus mean firing rate and recruitment threshold versus derecruitment threshold relationships.EMG amplitude from BB(bout 1 vs.bout 2=65.71%±22.92%vs.43.05%±18.97%,p=0.015,d=1.077)and the y-intercept(group merged)from the MU recruitment threshold versus derecruitment threshold relationship(bout 1 vs.bout 2=7.10±14.20 vs.0.73±16.24,p=0.029,d=0.513)at 50%MVIC were significantly different between two bouts.However,other muscle damage markers did not show any CL-RBE in both muscle groups.Therefore,despite changes in muscle excitation and MU firing behavior,our results do not support the existence of CL-RBE on BB and FDI muscles.

Chronic training status affects muscle excitation of the vastus lateralis during repeated contractions

This study examined electromyographic amplitude(EMGRMS)-force relationships during repeated submaximal knee extensor muscle actions among chronic aerobically-(AT),resistance-trained(RT),and sedentary(SED)individuals.Fifteen adults(5/group)attempted 20 isometric trapezoidal muscle actions at 50%of maximal strength.Surface electromyography(EMG)was recorded from vastus lateralis(VL)during the muscle actions.For the first and last successfully completed contractions,linear regression models were fit to the log-transformed EMGRMS-force relationships during the linearly increasing and decreasing segments,and the b terms(slope)and a terms(antilog of y-intercept)were calculated.EMGRMS was averaged during steady force.Only the AT completed all 20 muscle actions.During the first contraction,the b terms for RT(1.3010.197)were greater than AT(0.9100.123;p?0.008)and SED(0.9120.162;p?0.008)during the linearly increasing segment,and in comparison to the linearly decreasing segment(1.0180.139;p?0.014),respectively.For the last contraction,the b terms for RT were greater than AT during the linearly increasing(RT?1.3730.353;AT?0.8830.129;p?0.018)and decreasing(RT?1.5260.328;AT?0.9700.223;p?0.010)segments.In addition,the b terms for SED increased from the linearly increasing(0.9680.144)to decreasing segment(1.2680.126;p?0.015).There were no training,segment,or contraction differences for the a terms.EMGRMS during steady force increased from the first-([64.0851.68]μV)to last-contraction([86.7349.55]μV;p?0.001)collapsed across training statuses.The b terms differentiated the rate of change for EMGRMS with increments in force among training groups,indicating greater muscle excitation to the motoneuron pool was necessary for the RT than AT during the linearly increasing and decreasing segments of a repetitive task.