Estimation of shape characteristics of surface muscle signal spectra from time domain data

A Novel HD-sEMG Preprocessing Method Integrating Muscle Activation Heterogeneity Analysis and Kurtosis-Guided Filtering for High-Accuracy Joint Force Estimation

This study proposes a novel preprocessing method integrating muscle activation heterogeneity analysis and kurtosis-guided filtering to realize high-accuracy surface electromyogr-aphy (sEMG)-based force estimation. A total of 10 subjects were recruited. Each subject performed isometric elbow flexion tasks at 20%, 40%, and 60% maximum voluntary contraction (MVC) target force levels, and the joint force and high-density sEMG (HD-sEMG) signals from biceps brachii and brachialis were collected synchronously. The force estimation model was built using three-order polynomial fitting technique. The input signal extraction of the force model, also named as the preprocessing of HD-sEMG signal, was carried out in the following procedures: first, HD-sEMG signals were decomposed by principal component analysis into principal components and weight vectors; second, the first several weight maps were segmented to obtain heterogeneity information by the Otsu and Moore-Neighbor tracing methods, and the principal component covering the most activated areas was selected; and last, a kurtosis-guided filter was designed to process the selected principal component to obtain the input signal. For the sake of comparison, the joint force estimation experiments based ON five preprocessing methods were conducted. The experimental results demonstrated that the proposed method obtained 52%, 53%, and 59% reduction in the mean root mean square difference at 20% MVC, 40% MVC, and 60% MVC force-level tasks, respectively, compared to the preprocessing method with the first principal component plus fixed parameter filtering. This proposed HD-sEMG pre-processing method has reliable neuromuscular electro-physiological foundation, and has good application value for realizing high-accuracy muscle/joint force estimation in the fields of rehabilitation engineering, sports biomechanics, and muscle disease diagnosis etc.

Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar

OBJECTIVES

The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified.

METHODS

A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water.

RESULTS

Our proposed pattern classification system achieved accuracies of up to 93% in stationary subject detection, 90% in stop-breathing detection, and 86% in interference detection.

CONCLUSION

Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences.

SIGNIFICANCE

Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection.

The effects of head movement on dual-axis cervical accelerometry signals

Background

Head motions can severely affect dual-axis cervical acceloremetry signals. A complete understanding of the effects of head motion is required before a robust accelerometry-based medical device can be developed. In this paper, we examine the spectral characteristics of dual-axis cervical accelerometry signals in the absence of swallowing but in the presence of head motions.

Findings

Data from 50 healthy adults were collected while participants performed five different head motions. Three different spectral features were extracted from each recording: peak frequency, spectral centroid and bandwidth. Statistical analyses showed that peak frequencies are independent of the type of head motion, participant gender and age. However, spectral centroids are statistically different between the anterior-posterior (A-P) and superior-inferior (S-I) directions and between different motion. Additionally, statistically different bandwidths are observed for head tilts down and back between the A-P and the S-I directions.

Conclusions

These differences indicate that head motions induce additional non-dominant spectral components in dual-axis cervical recordings. The results presented here suggest that head motion ought to be considered in the development of medical devices based on dual-axis cervical accelerometery signals.

Surface EMG based muscle fatigue evaluation in biomechanics

In the last three decades it has become quite common to evaluate local muscle fatigue by means of surface electromyographic (sEMG) signal processing. A large number of studies have been performed yielding signal-based quantitative criteria of fatigue in primarily static but also in dynamic tasks. The non-invasive nature of this approach has been particularly appealing in areas like ergonomics and occupational biomechanics, to name just the most prominent ones. However, a correct appreciation of the findings concerned can only be obtained by judging both the scientific value and practical utility of methods while appreciating the corresponding advantages and limitations. The aim of this paper is to serve as a state of the art summary of this issue. The paper gives an overview of classical and modern signal processing methods and techniques from the standpoint of applicability to sEMG signals in fatigue-inducing situations relevant to the broad field of biomechanics. Time domain, frequency domain, time-frequency and time-scale representations, and other methods such as fractal analysis and recurrence quantification analysis are described succinctly and are illustrated with their biomechanical applications, research or clinical alike. Examples from the authors' own work are incorporated where appropriate. The future of this methodology is projected by estimating those methods that have the greatest chance to be routinely used as reliable muscle fatigue measures.

Comparison of heart rate variability signal features derived from electrocardiography and photoplethysmography in healthy individuals

The heart rate variability (HRV) signal is indicative of autonomic regulation of the heart rate (HR). It could be used as a noninvasive marker in monitoring the physiological state of an individual. Currently, the primary method of deriving the HRV signal is to acquire the electrocardiogram (ECG) signal, apply appropriate QRS detection algorithms to locate the R wave and its peak, find the RR intervals, and perform suitable interpolation and resampling to produce a uniformly sampled tachogram. This process could sometimes result in errors in the HRV signal due to drift, electromagnetic and biologic interference, and the complex morphology of the ECG signal. The photoplethysmographic (PPG) signal has the potential to eliminate the problems with the ECG signal to derive the HRV signal. To investigate this point, a PDA-based system was developed to simultaneously record ECG and PPG signals to facilitate accurately controlled sampling and recording durations. Two healthy young volunteers participated in this pilot study to evaluate the applicability of our approach. To improve data quality, ECG and PPG recordings were acquired three times/subject. A comparison between different features of the HRV signals derived from both methods was performed to test the validity of using PPG signals in HRV analysis. We used autoregressive (AR) modeling, Poincare' plots, cross correlation, standard deviation, arithmetic mean, skewness, kurtosis, and approximate entropy (ApEn) to derive and compare different measures from both ECG and PPG signals. This study demonstrated that our PDA-based system was a convenient and reliable means for acquisition of PPG-derived and ECG-derived HRV signals. The excellent agreement between different measures of HRV signals acquired from both methods provides potential support for the idea of using PPGs instead of ECGs in HRV signal derivation and analysis in ambulatory cardiac monitoring of healthy individuals.

Compression of surface EMG signals with algebraic code excited linear prediction

Despite the interest in long timescale recordings of surface electromyographic (EMG) signals, only a few studies have focused on EMG compression. In this paper we investigate a lossy coding technique for surface EMG signals that is based on the algebraic code excited linear prediction (ACELP) paradigm, widely used for speech signal coding. The algorithm was adapted to the EMG characteristics and tested on both simulated and experimental signals. The coding parameters selected led to a compression ratio of 87.3%. For simulated signals, the mean square error in signal reconstruction and the percentage error in average rectified value after compression were 11.2% and 4.90%, respectively. For experimental signals, they were 6.74% and 3.11%. The mean power spectral frequency and third-order power spectral moment were estimated with relative errors smaller than 1.23% and 8.50% for simulated signals, and 3.74% and 5.95% for experimental signals. It was concluded that the proposed coding scheme could be effectively used for high rate and low distortion compression of surface EMG signals. Moreover, the method is characterized by moderate complexity (approximately 20 million instructions/s) and an algorithmic delay smaller than 160 samples (approximately 160ms).

Spectral moments of mechanomyographic signals recorded with accelerometer and microphone during sustained fatiguing contractions

The aim of this study was to analyse the trends of the first three power spectral moments of the mechanomyogram (MMG) signal recorded by a microphone (MMG(MIC)) and an accelerometer (MMG(ACC)) during sustained contractions. MMG signals were recorded from the biceps brachii muscle in 14 healthy male subjects during a 3 min isometric elbow flexion at 30% of the maximal voluntary contraction. MMG absolute and normalised root mean square (RMS), mean power frequency (MNF), power spectral variance (Mc2), and skewness (mu3) were computed. For both MMG(MIC) and MMG(ACC), absolute and normalised RMS and Mc2 increased while MNF and mu3 decreased with contraction time (P<0.001). The rates of change of RMS over time were significantly correlated (P<0.001) for MMG(MIC) and MMG(ACC) but not correlated for spectral moments. The coefficient of variation of RMS was higher for MMG(MIC) than for MMG(ACC), while the opposite was observed for mu3 (P<0.05). It was concluded that higher order spectral moments of the MMG signal change during sustained contraction, indicating a complex modification of the shape of the power spectrum and not just scaling of the bandwidth. This is most likely due to the additional motor unit recruitment with fatigue and to the non-linear summation of motor unit contributions to the signal. Moreover, the characteristics of MMG signals recorded with microphones and accelerometers have important differences, which should be taken into account when comparing results from different studies.

An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions

During a sustained contraction, electromyographic signals (EMGs) undergo a spectral compression. This fatigue behaviour induces a shift of the mean and the median frequencies to lower frequencies. On the other hand, several studies conclude that the mean/median frequency can increase, decrease or remain constant with an increasing force level. Such inconsistency is embarrassing since the fatigue state may be influenced by the force level. In this paper, we propose a frequency indicator which is sensitive to the force level independently of the fatigue state evaluated at 70% of the maximal voluntary contraction. Ten healthy volunteers participated in the study and both surface EMGs (from the short head of the biceps brachii) and force signals were measured. This study compared force and fatigue effects on the EMGs during short (3-s) isometric contractions at different strength intensities and during a sustained isometric contraction until exhaustion. The EMGs partly show 1/falpha spectral behaviours since their power spectral densities may experimentally fit with two linear segments in a log-log representation. The measured "right" slope produces variations of force as 20 times the variations of fatigue. 1/falpha Behaviour may be related to stochastic fractals. This fractal indicator is a new frequency indicator that is thus complementary to other known classical frequency indicators when studying force during unknown fatigue states.

Improved Maximum Frequency Estimation With Application to Instantaneous Mean Frequency Estimation of Surface Electromyography

The purpose of this study was to improve the maximum-frequency estimation. Three methods to estimate the maximum frequency of a bandlimited signal with additive white noise were compared. Two existing methods, the threshold-crossing method (TCM) and the hybrid method, were modified for time-frequency representations. A novel approach, the running-block threshold method (RBTM), was introduced. Based on calculation of detection probability (sensitivity) the RBTM improved the maximum-frequency estimate as compared with the TCM. The maximum-frequency estimation methods were also used to determine the integration interval for instantaneous mean-frequency (IMNF) estimation from synthesized surface electromyography containing white noise. Results showed that the IMNF estimate was improved by using any of the three methods and that the RBTM gave the best IMNF estimate.

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.

Enhancement of spectral analysis of myoelectric signals during static contractions using wavelet methods

In this paper, we introduce wavelet packets as an alternative method for spectral analysis of surface myoelectric (ME) signals. Both computer synthesized and real ME signals are used to investigate the performance. Our simulation results show that wavelet packet estimate has slightly less mean square error (MSE) than Fourier method, and both methods perform similarly on the real data. Moreover, wavelet packets give us some advantages over the traditional methods such as multiresolution of frequency, as well as its potential use for effecting time-frequency decomposition of the nonstationary signals such as the ME signals during dynamic contractions. We also introduce wavelet shrinkage method for improving spectral estimates by significantly reducing the MSE's for both Fourier and wavelet packet methods.