Apostolos Georgakis

Fatigue analysis of the surface EMG signal in isometric constant force contractions using the averaged instantaneous frequency

The averaged instantaneous frequency (AIF) is proposed as an alternative method for the frequency analysis of surface electromyography (EMG) in the study of muscle fatigue during sustained, isometric muscle contractions. Results from performance analysis using experimental EMG signals demonstrate the low variability of the proposed frequency variable. Indeed, the AIF measure is shown to perform significantly better than the widely used mean and median frequency variables, in terms of robustness to the length of the analysis window.

Impact phase kinematics of instep kicking in soccer

Abstract The purpose of this study was to capture the lower limb kinematics before during and after ball impact of soccer kicking by examining the influence of both sampling rate and smoothing procedures. Nine male soccer players performed maximal instep kicks and the three-dimensional leg movements were captured at 1000 Hz. Angular and linear velocities and accelerations were determined using four different processing approaches: processed using a modified version of a time-frequency filtering algorithm (WGN), smoothed by a second-order low-pass Butterworth filter at 200 Hz cut-off (BWF), re-sampled at 250 Hz without smoothing (RSR) and re-sampled at 250 Hz but filtered by the same Butterworth filter at 10 Hz cut-off (RSF). The WGN approach appeared to establish representative kinematics, whereas the other procedures failed to remove noisy oscillation from the baseline of signal (BWF), lost the peaks of rapid changes (RSR) or produced totally distorted movement patterns (RSF). The results indicate that the procedures used by some previous studies may have been insufficient to adequately capture the lower limb motion near ball impact. We propose a new time-frequency filtering technique as a better way to smooth data whose frequency content varies dramatically.

Filtering in Rotated Time-Frequency Domains With Unknown Noise Statistics

The concept of rotation in the joint time-frequency plane can be exploited in order to generalize classical Fourier-based operations. It is known that filtering in rotated time-frequency domains can lead to significant performance advantages for certain types of signals as compared to conventional linear time invariant systems. In this correspondence, we revisit the design problem of such a scheme and derive a formulation that does not require knowledge of the statistics of the corrupting noise. Simulations have been used to confirm the validity of the proposed solution.

Interference Suppression in the Wigner Distribution Using Fractional Fourier Transformation and Signal Synthesis

A novel method for the suppression of cross terms in the time-frequency domain is introduced. First, interference is identified using a fractional Fourier transform-based technique. Then, auto-terms are detected, synthesized, and subtracted from the original signal. The process is repeated until all signal components are extracted. Finally, the Wigner distributions of pure auto-terms are superimposed to yield a high readability representation

Estimation of the Second Derivative of Kinematic Impact Signals Using Fractional Fourier Domain Filtering

A new filtering algorithm is proposed for the accurate estimation of the second derivatives of kinematic signals with impacts. The algorithm operates in predetermined consecutive fractional Fourier transform domains and amounts to an overall linear low-pass filter with time-varying cutoff threshold, which can successfully accommodate the impact-induced changes in the frequency content of the signals. The proposed method was applied to experimentally acquired displacement data and the results have demonstrated its promising performance that was found superior to both conventional techniques and recently introduced advanced schemes.

Wigner filtering with smooth roll-off boundary for differentiation of noisy non-stationary signals

Noise filtering in the time-frequency domain using a smooth roll-off boundary in the Wigner function is shown to yield substantial benefits for accurate signal differentiation.

Automatic algorithm for filtering kinematic signals with impacts in the Wigner representation

An automatic filtering algorithm is proposed for the accurate estimation of the second derivatives of kinematic signals with impacts. The impacts considered here occur when a moving object hits a rigid surface. The algorithm performs time-frequency filtering in the Wigner representation, to deal efficiently with the non-stationarities caused by such impacts, and adjusts the parameters of its time-frequency filtering function so that the filtering process adapts to the individual characteristics of the signal in hand. Performance analysis and comparative evaluation with experimentally acquired kinematic impact signals demonstrated a higher accuracy, with performance advantages over two widely used conventional automatic methods: linear phase autoregressive model-based derivative assessment (LAMBDA) and generalised cross-validation using quintic splines (GCVQS). For high impacts, the average absolute relative error in estimating the peak acceleration was 5.7% with the proposed method, 17.2% with a Butterworth low-pass filter optimised to yield minimum overall acceleration RMS error (best-case result), 18.3% with the LAMBDA method, and 37.2% with the GCVQS method. For signals with low impacts, the average absolute relative error was 19.4%, 6.9%, 8.3% and 19.1%, respectively, in each case, which indicates that, for signals with a low-frequency content, there is no need for such time-frequency filtering.

Enhancing the Resolution of the Spectrogram Based on a Simple Adaptation Procedure

This work is concerned with improving the quality of signal localization for the short-time Fourier transform by properly adjusting the size of its analysis window over time. The adaptation procedure involves the estimation of an area in the time-frequency plane which is more compact than the support of the fixed-window spectrogram. Then, at each time instant, the optimal window is selected such that the signal energy is maximized within the identified area. The proposed method achieves its objectives, and can compare favorably with alternative time-adaptive spectrograms as well as with advanced quadratic representations.

Optimal design of Hermitian transform and vectors of both mask and window coefficients for denoising applications with both unknown noise characteristics and distortions

This paper proposes an optimal design of a Hermitian transform and vectors of both mask and window coefficients for denoising signals with both unknown noise characteristics and distortions. The signals are represented in the vector form. Then, they are transformed to a new domain via multiplying these vectors to a Hermitian matrix. A vector of mask coefficients is point by point multiplied to the transformed vectors. The processed vectors are transformed back to the time domain. A vector of window coefficients is point by point multiplied to the processed vectors. An optimal design of the Hermitian matrix and the vectors of both mask and window coefficients is formulated as a quadratically constrained programming problem subject to a Hermitian constraint. By initializing the window coefficients, the Hermitian matrix and the vector of mask coefficients are derived via an orthogonal Procrustes approach. Based on the obtained Hermitian matrix and the vector of mask coefficients, the vector of window coefficients is derived. By iterating these two procedures, the final Hermitian matrix and the vectors of both mask and window coefficients are obtained. The convergence of the algorithm is guaranteed. The proposed method is applied to denoise both clinical electrocardiograms and electromyograms as well as speech signals with both unknown noise characteristics and distortions. Experimental results show that the proposed method outperforms existing denoising methods.

Fractional fourier-based filter for denoising elastograms

In ultrasound elastography, tissue axial strains are obtained through the differentiation of axial displacements. However, the application of the gradient operator amplifies the noise present in the displacement rendering unreadable axial strains. In this paper a novel denoising scheme based on repeated filtering in consecutive fractional Fourier transform domains is proposed for the accurate estimation of axial strains. The presented method generates a time-varying cutoff threshold that can accommodate the discrete non-stationarities present in the displacement signal. This is achieved by means of a filter circuit which is composed of a small number of ordinary linear low-pass filters and appropriate fractional Fourier transforms. We show that the proposed method can improve the contrast-to-noise ratio (CNRe) of the elastogram outperforming conventional low-pass filters.