A pseudoperiodic model for myoelectric signal during dynamicexercise

A theoretical model of dynamic surface myoelectric signal (SMES) suitable for periodic exercise has been developed. It has been shown that the spectral content of SMES in the very low-frequency band can be related to burst patterns of SMES. Burst patterns were compared during cycling exercises at different pedaling rates. A simple spectral parameter, defined in the very low-frequency band, was related to movement efficiency.     

Adaptive machine learning algorithm employed statistical signal processing for classification of ECG signal and myoelectric signal

Multidimensional Systems and Signal Processing - In this research paper we present designing and evaluating the electrocardiography (ECG) and Myoelectric signal (EMG) pattern recognition methods...     

Single motor unit myoelectric signal analysis with nonstationarydata

The information content of the myoelectric signal (MES) is commonly revealed by statistical measures in the time or frequency domain. Empirical analyses of the MES from a single motor unit have generally assumed that features are invariant with time. Theoretical and experimental work has been done to demonstrate how nonstationary behavior in the discharge statistics of a motor neuron may affect estimates of features extracted from the motor unit's contribution to the MES. Specifically, it has been shown that nonstationary behavior can markedly influence estimates of features describing motor neuron firing behavior and consequently, the low-frequency portion of the MES power spectral density. These results may help to explain the discrepancies in the literature which report empirical models of motor neuron firing statistics     

On-line estimation of myoelectric signal spectral parameters andnonstationarities detection

A method for detecting nonstationarities of random time series with an approximately Gaussian distribution of amplitudes is presented. This method is suitable for real time implementation. Some results obtained by applying them to a time series of spectral parameters of surface myoelectric signals are reported. The computerized system used to implement the detector of nonstationarity is described. This system realizes on-line estimation and display of spectral parameters, as well as detection of their nonstationarities, featuring a sampling frequency up to 20 k samples/s. A user friendly interface, fully menu driven, allows the user to select different options during the system's operation by means of hot keys. The accuracy of the system was tested by comparing its estimates with those of an off-line system, previously characterized. The estimates of spectral parameters obtained by means of the two systems were always consistent. The on-line stationarity detector was able to recognize rates of variation of the spectral parameters as small as 1% during contractions lasting 10-15 s. This sensitivity makes it suitable for clinical application     

A comparison of surface and intramuscular myoelectric signal classification

The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement should have less muscular crosstalk allowing for more independent control sites. However, it remains unclear if this benefit outweighs the loss of the more global information contained in the surface MES. This paper compares the classification accuracy of six pattern recognition-based myoelectric controllers which use multi-channel surface MES as inputs to the same controllers which use multi-channel intramuscular MES as inputs. An experiment was designed during which surface and intramuscular MES were collected simultaneously for 10 different classes of isometric contraction. There was no significant difference in classification accuracy as a result of using the intramuscular MES measurement technique when compared to the surface MES measurement technique. Impressive classification accuracy (97%) could be achieved by optimally selecting only three channels of surface MES.     

多通道表面肌电信号同步采样与处理研究

在康复机器人的人机交互系统中,表面肌电信号(sEMG)发挥着重要作用。针对采集单通道的肌电信号已经不能满足获取更多信息量的需求,该文设计一个8通道的肌电信号同步采集系统。该系统包括表面电极贴片、仪表放大器、带通滤波与二级放大、50 Hz陷波器、同步采集ADC和无线蓝牙模块等部分。通过测试实验,该采集系统很好地采集了人体的表面肌电信号,有效去除了共模噪声和50 Hz的工频干扰。获取的同步肌电信号可以进一步用于人机交互系统的模式识别研究。     

Surface myoelectric signal analysis: dynamic approaches for change detection and classification

Toward the goal of elbow and wrist prostheses control by characterizing events in surface myoelectric signals, this paper presents a dynamic method to simultaneously detect and classify such events. Dynamic cumulative sum of local generalized likelihood ratios using wavelet decomposition of the myoelectric signal is used for on-line detection. Frequency as well as energy changes are detected with this hybrid approach. Classification is composed of using multiresolution wavelet analysis and autoregressive modeling to extract signal features while polynomial classifiers are used for pattern modeling and matching. The results of detecting and classifying four elbow and wrist movements show that, in average, 91% of the events are correctly detected and classified using features obtained from multiresolution wavelet analysis while 95% accuracy is achieved with AR modeling. The classification accuracy decreases, however, if short prostheses response delay is desired. This paper also shows that the performance of the polynomial classifiers is better than that of the commonly used neural networks since it gives higher classification accuracy and consistent classification outcomes. In comparison to the well known support vector machine classification, the polynomial classifier gives similar results without the need to optimize and search for classifier parameters.     

A statistical method for the measurement of muscle activationintervals from surface myoelectric signal during gait

The aim of this work is to present an original double-threshold detector of muscle activation, specifically developed for gait analysis. This detector operates on the raw myoelectric signal and, hence, it does not require any envelope detection. Its performances are fixed by the values of 3 parameters, namely, false-alarm probability (P_fa), detection probability, and time resolution. Double-threshold detectors are preferable to single-threshold ones because, for a fixed value of the P_fa, they yield higher detection probability; furthermore, they allow the user to select the couple false alarm-detection probability with a higher degree of freedom, thus, adapting the performances of the detector to the characteristics of the myoelectric signal of interest and of the experimental situation. Here, first the authors derive the detection algorithm and describe different strategies for selecting its parameters, then they present the performances of the proposed procedure evaluated by means of computer simulations, and finally they report an example of application to myoelectric signals recorded during gait. The characterization of the proposed double-threshold detector demonstrates that, in most practical situations, the bias of the estimates of the on-off transitions is smaller than 10 ms, the standard deviation may be kept lower than 15 ms, and the percentage of erroneous patterns is below 5%. These results show that this detection approach is satisfactory in research applications as well as in the clinical practice     

测量光电流信号的一种简单方法

本文介绍用运算放大器和RCM络构成的带通滤波器测量光电信号及其结果，此方法简单易行，适当选择取样电阻．可使误差小于2％。     

Time-varying techniques for multisensor signal detection

In source detection and localization, the presence of a common but unknown signal can be detected from several noisy sensor measurements using the generalized coherence (GC) estimate. We propose to improve the performance of the GC estimate for multisensor detection using noise-suppressed signal estimates obtained from time-varying techniques. If one of the sensors has a significantly higher signal-to-noise ratio (SNR) than the other sensors, then it could be preprocessed prior to the GC estimate to improve detection performance for the remaining, lower SNR sensors. We perform this processing by estimating time-varying signals of interest with nonlinear phase functions using two methods: a) a modified matching pursuit decomposition (MMPD) algorithm whose dictionary is similar, in time-frequency structure, to the signal and b) an instantaneous frequency (IF) estimation method using highly localized time-frequency representations. The MMPD can yield signal estimates with lower mean square errors than the IF estimation technique but at the expense of higher computational cost and memory requirements. Using simulations, we compare the performance of the GC estimate with the significantly improved performance of the GC estimate that employs the signal estimate from the high SNR sensor. For the two-sensor detection, the estimated signal is also used with a generalized likelihood ratio test statistic to further improve performance.     

光纤测试技术新发展

光纤通信技术的发展，促进了诸如光谱特性、偏振模色散(PMD)、非线性、色散均匀性等光纤测试新技术的研发以及光时域反射计(OTDR)，差分模延迟(DMD)等原有测试技术的拓展。为此对上述技术作了简要介绍和归纳。     

一种多路信号幅值测量系统

本文介绍了一种在单片机支持下实现多路信号幅值同时测量和显示的设计,该设计在实际应用中已获得满意效果.     

A TECHNIQUE FOR MEASURING THE LINEARITY OF A LINEARLY FREQUENCY-MODULATED SIGNAL

A technique for measuring the linearity of a linearly frequency-modulated continuous wave (LFM-CW) signal is presented. It uses a delay-line and a mixer to sense the slope of the output of a sweep oscillator, so that the original form of frequency function deviated from idealized linear slope is retrieved by means of spectrum analysis. Consequently,the linearity of the LFM signal is determined. The formulation is performed based on the principle that an angle-modulated signal can be approximated by an amplitude-modulated signal if the modulation coefficient is sufficiently small. To examine the validity of the procedure and to study the effect of each parameter on the accuracy of measurement, a number of computer simulations has been made. The results of simulation show that the error of the measurement is less than 2%.     

Time-frequency parameters of the surface myoelectric signal forassessing muscle fatigue during cyclic dynamic contractions

The time-dependent shift in the spectral content of the surface myoelectric signal to lower frequencies has proven to be a useful tool for assessing localized muscle fatigue. Unfortunately, the technique has been restricted to constant-force, isometric contractions because of limitations in the processing methods used to obtain spectral estimates. A novel approach is proposed for calculating spectral parameters from the surface myoelectric signal during cyclic dynamic contractions. The procedure was developed using Cohen class time-frequency transforms to define the instantaneous median and mean frequency during cyclic dynamic contractions. Changes in muscle length, force, and electrode position contribute to the nonstationarity of the surface myoelectric signal. These factors, unrelated to localized fatigue, can be constrained and isolated for cyclic dynamic contractions, where they are assumed to be constant for identical phases of each cycle. Estimation errors for the instantaneous median and mean frequency are calculated from synthesized signals. It is shown that the instantaneous median frequency is affected by an error slightly lower than that related to the instantaneous mean frequency. In addition, we present a sample application to surface myoelectric signals recorded from the first dorsal interosseous muscle during repetitive abduction/adduction of the index finger against resistance. Results indicate that the variability of the instantaneous median frequency is related to the repeatability of the biomechanics of the exercise.     

System identification techniques for adaptive signal processing

Many problems in adaptive filtering can be approached from the point of view of system identification. The close interconnection between these two disciplines is explored in some detail. This approach makes it possible to apply recursive parameter estimation algorithms to adaptive signal processing. Several examples are discussed including: adaptive line enhancement, generalized adaptive noise cancelling, adaptive deconvolution and adaptive TDOA estimation. It is shown how the recursive maximum likelihood algorithm can be used for both FIR and IIR filtering, and some preliminary results are presented. Several alternative algorithms are briefly discussed.This work was supported by the Office of Naval Research, Contract No. N00014-79-C-0743.     

One-dimensional signal processing techniques for airborne laserbathymetry

The application of one-dimensional signal processing techniques for the preprocessing of waveforms obtained in airborne laser bathymetry is investigated. Specifically, a special type of smoothing digital filter is used to remove noise in the waveforms while preserving the information content up to a desired degree. An algorithm, which incorporates a lowpass digital differentiator is then used to detect the bottom reflection. The optimal cutoff frequency of the differentiator is determined on the basis of the spectral content of the bottom reflection. After preprocessing, a waveform-decomposition technique recently described by the authors is used to separate the surface and bottom reflections for sea-depth estimation. In order to evaluate the effectiveness of the techniques developed, sea-depth estimates obtained are compared to corresponding estimates obtained by a local surveying company, and a high degree of agreement is observed between the two sets of results. When the resolution between the surface and bottom returns is low, the proposed techniques together with waveform decomposition offer a significant improvement in the sea-depth estimates     

Part I--Digital techniques for communication signal processing

Digital processing of communication signals is now a practical alternative to analog processing. This application of a well-understood theory as a viable alternative is largely due to the availability of low cost arithmetic and storage LSI circuits; the availability of low cost A/D and D/A circuits; the commonplace occurrence of digital signals due to the ever increasing use of digital transmission and routing; and the significant advantages offered by the flexibility of the digital approach. This paper is an attempt to present in an introductory manner the what, why, where, and how of digital signal processing.     

Two-dimensional signal processing techniques for airborne laserbathymetry

The application of two-dimensional (2D) signal processing to data collected in airborne laser bathymetry is investigated. Specifically, a type of 2D filter for the suppression of impulsive noise in irregularly-spaced data based on order-statistics filtering is developed. An algorithm which incorporates this type of filter along with a sophisticated 2D interpolation technique is constructed to automate the filtering process. An adaptive 2D filtering technique that can be applied to raw bathymetric profiles to remove wideband noise is then discussed. The results obtained show that each type of filtering enhances the accuracy of bathymetric measurement quite significantly     

ASIC techniques for high-performance digital signal processing

We describe a high-level ASIC (application specific integrated circuit) synthesis system aimed at rapid and efficient realisation of integer arithmetic “engines” for signal processing bottleneck computations. Novel software features include bit-level scheduling which allocates numerical resources for computation, and a parameter synthesis system which maximises the use of this resource. Underlying synthesis is a generic digit-serial integer arithmetic processing architecture, with module generation capability across a wide parameter space for a useful set of primitive arithmetic operations. We outline the principal components of the tool, and briefly describe some application examples.