Multivariate analysis of muscular fatigue during bicycle ergometerexercise

The purpose of this study is to estimate the endurance threshold in terms of muscular fatigue during bicycle ergometer exercise. The problems to be solved are induced by dynamic movement and the physiological variation of muscle activity: that is, the progression and impairment of muscle activity occur simultaneously. First of all, the authors used multichannel recordings of myoelectric (ME) signals to reduce the effect by the movement of a bipolar surface electrode relative to the innervation zones. Second, since even the different types of ME parameters contain redundant information on muscular fatigue, the authors used the principal component analysis (PCA) to represent the meaningful information by small dimensions. Moreover, the authors proposed a total evaluation pattern to discriminate muscular fatigue from progression of muscle force at a glance. The total evaluation pattern shows the proportion of first principal component, the components of the first eigenvector, and the correlation coefficients as a function of the work load. The assessment using the total evaluation pattern divided 8 subjects into 3 groups, whereas these subjects were not identified by a specific ME parameter     

A Reliable Myoelectric Signal Detector Based on the Propagation Characteristics of Motor Unit Action Potentials

A method was proposed for detecting and rejecting motion artifacts superimposed on myoelectric (ME) signals which are used in the estimation of muscular activity, in the control of powered prostheses, and in other applications. The method is based on the propagation characteristics of motor unit action potentials derived with multiple surface electrodes placed along the muscle fibers. The contamination of artifacts was detected by the decrease of the normalized correlation coefficient calculated at the time shift corresponding to the potential propagation. The product of two correlated signals was found to be less affected by the artifacts and was a better estimate of muscular activity than the root mean square of the ME signal which is conventionally used in the applications of ME signals.     

Muscle Fatigue Monitor: A Noninvasive Device for Observing Localized Muscular Fatigue

As a muscular contraction is sustained, the spectrum of the myoelectric signal is compressed into lower frequencies. This spectral compression has been associated with localized muscular fatigue by several investigators. A device is presented that implements a technique to track the spectral compression by calculating the median frequency and two other parameters of the spectrum. The device is referred to as the muscle fatigue monitor (MFM) and is built with analog circuitry so that parameters are calculated and displayed in real-time and on-line. The technique is based on modulated filters which are implemented by using periodically-controlled switches to effectively vary the cutoff frequencies of the filters. The median frequency of a myoelectric signal obtained during a sustained, constant-force, isometric contraction was calculated by the MFM and digital computation. The results obtained by the two techniques were essentially the same, verifying the operation of the MFM.     

Muscle Fatigue Monitor (MFM): Second Generation

As a muscular contraction is sustained, the spectrum of the myoelectric signal is shifted toward the lower frequencies. This spectral shift is associated with localized muscular fatigue. This communication describes a computer-assisted device, the Muscle Fatigue Monitor, that performs a quantitative assessment of localized muscular fatigue by tracking changes in the median frequency parameter of the myoelectric signal's spectrum.     

Fatigue compensation of the electromyographic signal for prostheticcontrol and force estimation

During a sustained muscle contraction, the amplitude of electromyographic (EMG) signals increases and the spectrum of the EMG signal shifts toward lower frequencies. These effects are due to muscular fatigue and can cause problems in the control of myoelectric prostheses and in the estimation of contraction level from the EMG signal. It has been well known that the fatigue effects can be explained by the conduction velocity changes during the fatigue process and by the idea that the conduction velocity is linearly proportional to the median frequency of EMG signals. Hence the fatigue process can be monitored by measuring the median frequency. A fatigue compensation preprocessor has been developed. It uses the widely accepted power spectrum density model of EMG signals that contains the conduction velocity as a measure of fatigue. It was verified that the preprocessor scales down the amplitude of the fatigued EMG signal and decompresses the spectrum. Hence, the preprocessor eliminates the increase in amplitude and the shift in frequency and enables consistent EMG signals to be used to control prostheses     

The Measurement of Muscle Fiber Conduction Velocity Using a Gradient Threshold Zero-Crossing Method

The conduction velocity of myoelectric potential along muscle fiber is known to be an index of the degree of muscular fatigue or muscular disease. When detecting the myoelectric potential by means of surface electrodes, the conduction velocity must be extracted from an apparently random wave of a myoelectric signal. In this paper, a method for determining conduction velocity is proposed based upon a zero-crossing time delay measurement with reference to the derivative of a myoelectric signal. The slope value of the input signal provides an effective criterion for rejecting undesired zero crossing caused by noise. This method needs no spectral analysis nor correlation calculation. Compared to another previously reported zero-cossing approach using digital filter preprocessing, it shows a more accurate and rapid estimation of velocity.     

Modeling of surface myoelectric signals. II. Model-based signalinterpretation

Experimental electromyogram (EMG) data from the human biceps brachii were simulated using the model described in [10] of this work. A multichannel linear electrode array, spanning the length of the biceps, was used to detect monopolar and bipolar signals, from which double differential signals were computed, during either voluntary or electrically elicited isometric contractions. For relatively low-level voluntary contractions (10%-30% of maximum force) individual firings of three to four-different motor units were identified and their waveforms were closely approximated by the model. Motor unit parameters such as depth, size, fiber orientation and length, location of innervation and tendonous zones, propagation velocity, and source width were estimated using the model. Two applications of the model are described. The first analyzes the effects of electrode rotation with respect to the muscle fiber direction and shows the possibility of conduction velocity (CV) over- and under-estimation. The second focuses on the myoelectric manifestations of fatigue during a sustained electrically elicited contraction and the interrelationship between muscle fiber CV, spectral and amplitude variables, and the length of the depolarization zone. It is concluded that a) surface EMG detection using an electrode array, when combined with a model of signal propagation, provides a useful method for understanding the physiological and anatomical determinants of EMG waveform characteristics and b) the model provides a way for the interpretation of fatigue plots.     

Electrical manifestations of muscle fatigue during concentric and eccentric isokinetic knee flexion-extension movements

The quantification of the progression of muscle fatigue during a sustained contraction is a valuable tool in several clinical applications, ranging from the evaluation of functional impairment to the development of specific rehabilitative and training protocols. In these fields, great importance is given to isokinetic contractions. The aim of this paper was twofold: first, to propose signal processing methods for assessing the spectral changes of the surface myoelectric signal due to fatigue during isokinetic concentric and eccentric knee flexion-extension movements at a given angular velocity (60 degrees/s); second, to analyze the electrical manifestations of muscle fatigue of four thigh muscles (vastus lateralis, vastus medialis, rectus femoris, and biceps femoris) in the two contraction modalities (i.e. concentric versus eccentric). We demonstrated that, when considering concentric contractions, localized muscle fatigue can be assessed by computing the mean frequency of the frequency marginal of the time-frequency distribution derived from the surface myoelectric signal collected during each contraction cycle. Stronger nonstationarities were observed in the surface myoelectric data recorded within each cyclical movement of the studied eccentric exercise. Thus we propose the computation of the instantaneous mean frequency of the signal based on an original cross-time-frequency algorithm, which proved more sensitive than the frequency marginal in tracking the spectral changes associated with localized muscle fatigue. We derived the average fatigue pattern of the investigated muscles from experimental data recorded from a sample population consisting of twenty healthy subjects and we statistically compared the two contraction modalities. Our results showed that the electrical manifestations of muscle fatigue during concentric contractions were higher than those found during eccentric contractions, although in the latter modality the torque exerted and the mechanical work produced by the subjects were larger than those recorded during the concentric exercise. The results presented in this paper have potential clinical application and they could play an important future role in investigations of muscle behavior during dynamic, highly fatiguing contractions.     

Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study

In this paper, we introduce the nonstationary signal analysis methods to analyze the myoelectric (ME) signals during dynamic contractions by estimating the time-dependent spectral moments. The time-frequency analysis methods including the short-time Fourier transform, the Wigner-Ville distribution, the Choi-Williams distribution, and the continuous wavelet transform were compared for estimation accuracy and precision on synthesized and real ME signals. It is found that the estimates provided by the continuous wavelet transform have better accuracy and precision than those obtained with the other time-frequency analysis methods on simulated data sets. In addition, ME signals from four subjects during three different tests (maximum static voluntary contraction, ramp contraction, and repeated isokinetic contractions) were also examined.     

AR modeling of myoelectric interference signals during a rampcontraction

The authors investigated the time-varying behavior of the autoregressive (AR) parameters in a myoelectric (ME) signal detected during a linear force increasing contraction. The AR parameters of interest mere the reflection coefficients, the AR model spectrum, and the prediction errors. The authors used well-conditioned ME signals for which the complete time record of the motor units firings was available. In addition, the influence of the recruitment of a new motor unit, the conduction velocity of action potentials, and additive broad-band noise were investigated using simulated ME signals. The simulated ME signals were constructed from a selected group of the available motor unit action potential trains. The results revealed that, as the contraction progressed, the AR parameters displayed a time-varying behavior which coincided with the recruitment of newly recruited motor units whose spectrum of the waveform differed from that of the rest of the ME signal. This property of the AR parameters was obscured by the presence of broad-band noise and low-amplitude motor unit action potentials, both of which are more pronounced during low-level force contractions     

Changes in muscle activity and kinematics of highly trained cyclists during fatigue

Muscle fatigue may alter kinematics and contribute to repetitive strain injuries. This study quantified how both localized muscle fatigue and movement kinematics change over time during exhaustive cycling. Seven highly trained cyclists rode a stationary bicycle ergometer at 100% of their maximum oxygen consumption (VO(2) max) until voluntary exhaustion. Cycling kinematics and electromyography (EMG) activity from select lower extremity muscles were recorded. Cross-correlations were computed to quantify how EMG median frequencies (MDFs) changed with changes in movement kinematics. All athletes maintained both cadence and power output for approximately 90% of the trial duration. Significant sustained muscle fatigue occurred in 18 of 28 muscles tested, most prominently in the biceps femoris (p = 0.020) and gastrocnemius (p = 0.018). Kinematics and MDF both fluctuated nonmonotonically as subjects fatigued. Changes in MDF significantly preceded changes in mean trunk lean (p = 0.009) and hip angles (p = 0.025), and trunk lean range of motion ( p = 0.029). Fluctuations in MDF were positively correlated with fluctuations in mean trunk lean (p = 0.009) and knee splay angles (p = 0.011), and with trunk lean (p = 0.002) and ankle (p = 0.001) range of motion. These results therefore establish a direct link between changes in muscle fatigue state and subsequent changes in movement kinematics during cycling.     

Analysis and simulation of changes in EMG amplitude during high-level fatiguing contractions

Changes in surface electromyographic (EMG) amplitude during sustained, fatiguing contractions are commonly attributed to variations in muscle fiber conduction velocity (MFCV), motor unit firing rates, transmembrane action potentials and the synchronization or recruitment of motor units. However, the relative contribution of each factor remains unclear. Analytical relationships relating changes in MFCV and mean motor unit firing rates to the root mean square (RMS) and average rectified (AR) value of the surface EMG signal are derived. The relationships are then confirmed using model simulation. The simulations and analysis illustrate the different behaviors of the surface EMG RMS and AR value with changing MFCV and firing rate, as the level of motor unit superposition varies. Levels of firing rate modulation and short-term synchronization that, combined with variations in MFCV, could cause changes in EMG amplitude similar to those observed during sustained isometric contraction of the brachioradialis at 80% of maximum voluntary contraction were estimated. While it is not possible to draw conclusions about changes in neural control without further information about the underlying motor unit activation patterns, the examples presented illustrate how a combined analytical and simulation approach may provide insight into the manner in which different factors affect EMG amplitude during sustained isometric contractions.     

A muscle fatigue index based on the relationship between precedingbackground activity, and myotatic reflex response (MRR)

A new index of muscular fatigue was developed using the myotatic reflex response (MRR). The MRR of masseter muscles is evoked by periodical mechanical chin tapping during clenching. The MRR waveform is composed of somewhat synchronized action potentials of muscle fibers. We estimate the change of the MRR waveform due to fatigue using the instantaneous frequency pattern (IFP) with the Hilbert transform. As a result, the features of IFP were different from the monotonic changes that have been observed by the conventional fatigue indices. That is, a plateau IFP was observed ahead of considerable fatigue, whereas the IFP showed a monopeak pattern during other phase of an exercise. Also, the relationship between the preceding background mean power frequency and the instantaneous frequency around the first part of the MRR waveform was nonlinear during the whole process of fatigue. These features may allow us to estimate the degree of fatigue at each time instant. Although the details have not yet been solved, by using the computer simulations one of them seemed to be the alteration of the dominant frequency components. The dominant frequency components may be related to the active muscle fiber types.     

A subject-independent method for automatically grading electromyographic features during a fatiguing contraction

Many studies have attempted to monitor fatigue from electromyogram (EMG) signals. However, fatigue affects EMG in a subject-specific manner. We present here a subject-independent framework for monitoring the changes in EMG features that accompany muscle fatigue based on principal component analysis and factor analysis. The proposed framework is based on several time- and frequency-domain features, unlike most of the existing work, which is based on two to three features. Results show that latent factors obtained from factor analysis on these features provide a robust and unified framework. This framework learns a model from EMG signals of multiple subjects, that form a reference group, and monitors the changes in EMG features during a sustained submaximal contraction on a test subject on a scale from zero to one. The framework was tested on EMG signals collected from 12 muscles of eight healthy subjects. The distribution of factor scores of the test subject, when mapped onto the framework was similar for both the subject-specific and subject-independent cases.     

天波超视距雷达作战效能综合评估研究

为全面准确评估天波超视距雷达作战效能,提出了一种基于模糊层次决策的天波超视距雷达作战效能综合评估模型。建立了一种较为系统的天波超视距雷达作战效能综合评估指标体系。将层次分析法与模糊综合评判方法应用到雷达作战效能评估中,介绍了作战效能评估方法和步骤,最后以某型天波超视距雷达为例进行了实例应用与分析。结果表明,该评估模型实用有效,为天波超视距雷达的研制、改进、优化部署以及作战使用提供了辅助的决策依据。     

A comparative study of simultaneous vibromyography andelectromyography with active human quadriceps

Vibromyographic (VMG) signals, which are low-frequency vibration signals generated during muscle contraction, were studied in comparison with electromyographic (EMG) signals recorded simultaneously during isometric contraction of the human quadriceps muscles. The comparison was accomplished by evaluating the averaged root mean squared (rms) value, mean frequency (MF), and peak frequency (PF) of the VMG and EMG signals for four muscle contraction levels at joint angles of 30 degrees, 60 degrees, and 90 degrees. The four contraction levels, namely 20, 40, 60, and 80% of maximum voluntary contraction (MVC), were estimated and controlled by the torque readings of a Cybex II dynamometer. It was found that the VMG and EMG under the same conditions on the same muscle group are in general equally sensitive to the levels of muscle contraction. Results show that the rms value of the VMG signal increases linearly, in a manner similar to the EMG rms/%MVC relationship, with increasing muscle contraction levels. Furthermore, the study indicates that the averaged MF (6-24 Hz) and PF (9-19 Hz) of the VMG signals are much lower than the MF (75-109 Hz) and PF (40-80 Hz) of the EMG signals. The slopes of MF/%MVC curves for the VMG and EMG are approximately the same for 60 degrees and 90 degrees joint angles (approximately 3.1 Hz per 20% MVC for VMG and approximately 2.6 Hz per 20% MVC for EMG).(ABSTRACT TRUNCATED AT 250 WORDS)     

基于灰色关联与改进FAHP的产品优选模型

研究电脑与数码产品的科学选购问题。为了对评价指标进行定量分析,应用灰色关联分析与模糊层次分析法构造了一种产品优选模型。首先确立待选产品的评价指标特征量矩阵,计算各项指标与理想产品对应指标之间的关联系数;然后构建一个评价指标重要性两两比较矩阵,运用改进的模糊层次分析法确定各指标的权重;最后由关联系数与指标权重计算待选产品与理想产品之间的关联度,以此确定最优产品。实例结果表明,该模型较好地达到了预期目标,在批量采购电脑与数码产品时尤具实用价值。     

疲劳裂纹扩展的实时健康监测

结构中微裂纹的产生和扩展是影响结构安全及其寿命的重要因素,在裂纹产生初期就将其监测出来对于提高结构安全性来说至关重要。目前研究这一领域的学者很多,但大多都集中在孔洞等较大尺寸损伤的在线监测阶段。该文研究了真实疲劳裂纹扩展的实时在线监测方法。采用主动Lamb波监测技术及机电阻抗技术对铝试件的真实疲劳裂纹扩展情况进行了监测和分析,给出了裂纹扩展程度与信号损伤参数间的关系曲线,并对两种监测方法进行了对比,对实验结果进行了讨论。     

模糊层次法评估机载雷达抗干扰效能

为了评估机载雷达抗干扰效能,介绍了抗干扰性能特点,建立了抗干扰性能评估指标体系,利用层次分析法(AHP)计算了各品质因素的权重,用模糊数学理论建立了各指标因素的模糊隶属度函数,对机载雷达抗干扰效能进行了综合评估.该方法用精确的数学描述机载雷达系统抗干扰性能指标中的模糊性,为评判机载雷达抗干扰性能提供了一种新的方法.并对美军AN-APQ153和AN-APG69两种机载雷达抗干扰效能进行了模糊综合评估,实例分析结果表明该评估方法具有实用性,其评估方法可为机载雷达效能评估提供参考.     

Time-frequency analysis of skeletal muscle and cardiac vibrations

Skeletal muscle and the heart vibrate during contraction producing nonstationary signals whose time-varying frequency reflects dynamic changes in physiological properties. Consequently, pathological changes in the mechanical integrity or loading of skeletal muscle or the heart can be expected to alter their vibrations. Classic frequency analysis techniques have been inadequate to characterize these subtle changes because of rapidly varying frequency components. A poor understanding of heart and muscle sound generation has also limited investigations. This paper demonstrates how time-frequency (TF) techniques have illuminated the relationships between muscle/heart material properties and loading and frequency dynamics of heart and muscle vibrations. Studies of evoked twitches from frog skeletal muscle reveal that muscle vibrations occur as transverse oscillations at the muscle's resonant frequency. Using a classic Rayleigh-Ritz model and crude estimates of the muscle geometry, muscle force can be accurately predicted from the muscle sound TF profile. First heart sound vibrations, in contrast, are shown to be a nonresonant phenomena, consisting of propagating transients superimposed upon bulk acceleration of myocardial contraction. Consequently, first heart sound frequency dynamics depend upon cardiac electrical excitation and hemodynamic loading in addition to intrinsic material properties and geometry, necessitating further work to characterize pathophysiologic correlations