Teager-Kaiser Energy Operator Research Articles

Comparison of different algorithms based on TKEO for EMG change point detection

Objective. A significant challenge in surface electromyography (EMG) is the accurate identification of onset and offset of muscle activation while maintaining high real-time performance. Teager–Kaiser energy operator (TKEO) is widely used in muscle activity monitoring systems because of its computational simplicity and strong real-time performance. However, in contrast to TKEO ontology, few studies have examined how well the energy operator variants from multiple fields perform in conditioning EMG signals. This paper aims to investigate the role of the energy operator and its variants in EMG change point detection by a threshold detector. Approach. To compare the stability and accuracy of TKEO and its variants for EMG change point detection, the EMG data of extensor carpi radialis longus and flexor carpi radialis were acquired from twenty participants operating a controller under normal and disturbed conditions, and EMG change point detection was performed by four energy operators and their rectified versions. Main results. Based on the ‘standard’ change points collected by the controller, the detection results were evaluated by three evaluation indexes: detection rate, F1 Score, and accuracy. The experimental results show that the multiresolution energy operator and the TKEO with rectified (abs-TKEO) are more suitable for EMG change point detection. Significance. This paper compared the effect of the energy operator and its variants on a threshold-based EMG change point detector. The experimental results in this paper can provide a reference for the selection of EMG signal conditioning methods to improve the detection performance of the EMG change point detector.

Induction Motor Broken Rotor Bar Fault Diagnosis Based on Third-Order Energy Operator Demodulated Current Signal

Motor current signature analysis (MCSA) has been widely used for fault diagnosis of induction motors (IMs) because of its non-invasiveness and comparatively low cost. However, the spectral leakage of the fundamental supply frequency can obscure the fault characteristic component when IMs operating at no-load or low-load conditions. In this paper, a broken rotor bar (BRB) fault diagnosis approach based on the third-order energy operator (TOEO) demodulated current signals is presented. The TOEO approach performs effectively in reducing the negative impact of the fundamental supply frequency spectral leakage and enhancing the BRB fault features. Theoretical and experimental results show that the TOEO approach can excellently reveal the fault characteristic frequency, instead of the sideband frequencies. Besides, the results are compared with the Teager-Kaiser energy operator (TKEO). The capability of the presented approach has several advantages over the TKEO, especially when the IMs are fed by an inverter.

Modified Complete Ensemble Empirical Mode Decomposition based HIF detection approach for microgrid system

Detection of high impedance fault (HIF) in an active distribution system is a challenging task. It is learned from the fault characteristics that detection and discrimination of HIF during different critical conditions is impossible using the fault current magnitude. Under dependable situations such as energization of a transformer, nonlinear load and capacitor bank detection and discrimination of HIF is challenging. In a distribution system with an inverter based distributed generator (IBDG), the current contribution during islanding mode is very low and also for HIF condition. To mitigate these issues, an intelligent approach applying Modified Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (MCEEMDAN) on residual current signal is developed. The second intrinsic mode function (IMF2) is extracted using MCEEMDAN and its Teager Kaiser Energy Operator (TKEO) is computed to detect and discriminate the HIF against other physical events. The novelty of MCEEMDAN approach lies with its noise free output and faster response as compared to other time–frequency approaches. The method is tested for several fault and non-fault cases including, presence of noise, harmonics, and unbalance loadings. The comparison with recently reported techniques for very HIF and ungrounded system proofs the efficacy of the method.

Automatic detection of heart valve disorders using Teager–Kaiser energy operator, rational-dilation wavelet transform and convolutional neural networks with PCG signals

Automatic detection of heart valve disorders using Teager–Kaiser energy operator, rational-dilation wavelet transform and convolutional neural networks with PCG signals

IoMT-fog-cloud based architecture for Covid-19 detection

Limitations of available literatureNowadays, coronavirus disease 2019 (COVID-19) is the world-wide pandemic due to its mutation over time. Several works done for covid-19 detection using different techniques however, the use of small datasets and the lack of validation tests still limit their works. Also, they depend only on the increasing the accuracy and the precision of the model without giving attention to their complexity which is one of the main conditions in the healthcare application. Moreover, the majority of healthcare applications with cloud computing use centralization transmission process of various and vast volumes of information what make the privacy and security of personal patient’s data easy for hacking. Furthermore, the traditional architecture of the cloud showed many weaknesses such as the latency and the low persistent performance. Method proposed by the author with technical informationIn our system, we used Discrete Wavelet transform (DWT) and Principal Component Analysis (PCA) and different energy tracking methods such as Teager Kaiser Energy Operator (TKEO), Shannon Wavelet Entropy Energy (SWEE), Log Energy Entropy (LEE) for preprocessing the dataset. For the first step, DWT used to decompose the image into coefficients where each coefficient is vector of features. Then, we apply PCA for reduction the dimension by choosing the most essential features in features map. Moreover, we used TKEO, SHEE, LEE to track the energy in the features in order to select the best and the most optimal features to reduce the complexity of the model. Also, we used CNN model that contains convolution and pooling layers due to its efficacity in image processing. Furthermore, we depend on deep neurons using small kernel windows which provide better features learning and minimize the model's complexity.The used DWT-PCA technique with TKEO filtering technique showed great results in terms of noise measure where the Peak Signal-to-Noise Ratio (PSNR) was 3.14 dB and the Signal-to-Noise Ratio (SNR) of original and preprocessed image was 1.48, 1.47 respectively which guaranteed the performance of the filtering techniques.The experimental results of the CNN model ensure the high performance of the proposed system in classifying the covid-19, pneumonia and normal cases with 97% of accuracy, 100% of precession, 97% of recall, 99% of F1-score, and 98% of AUC. Advantages and application of proposed methodThe use of DWT-PCA and TKEO optimize the selection of the optimal features and reduce the complexity of the model.The proposed system achieves good results in identifying covid-19, pneumonia and normal cases.The implementation of fog computing as an intermediate layer to solve the latency problem and computational cost which improve the Quality of Service (QoS) of the cloud.Fog computing ensure the privacy and security of the patients’ data.With further refinement and validation, the IFC-Covid system will be real-time and effective application for covid-19 detection, which is user friendly and costless.

A novel time-domain method for fault detection and classification in VSC-HVDC transmission lines

The short circuit fault current is one of the worst contingencies in voltage source converter (VSC)-based high voltage direct current (HVDC) system. Therefore, having a fast, robust, and selective protection scheme is necessary in HVDC systems. In this paper, a non-communication-based protection scheme is presented using Intrinsic Time Decomposition (ITD) technique in a HVDC system. This time domain approach owns some prominent features such as high speed, low sampling rate and computational burden and no need for parameter's setting. The proposed protection scheme relies on the current signal sampled from the single end. The concept is to obtain the proper rotation component (PRC) of input signal and computing an energy index named Teager Kaiser Energy Operator (TKEO) to identify the fault occurrence in a HVDC line. The performance of the proposed scheme has been assessed against various scenarios including external disturbance such as ac faults and abrupt change of active power in two standard sample systems in MATLAB/SIMULINK and PSCAD/EMTDC environments. In addition, a wide range of internal dc faults are studied to evaluate the sensitivity of the scheme. The appropriate performance of the scheme is verified and the fault detection and classification tasks are done in less than 1 ms.

Enhancing the resiliency of transmission lines using extreme gradient boosting against faults

The study presented in the paper aims to reduce the relaying time associated with detection and classification of fault along with accurate fault location estimation with the help of Teager Kaiser energy operator (TKEO) and extreme gradient boost (XGBoost) algorithm. The complete analysis has been conducted on IEEE 14 Bus transmission network. Four XGBoost modules corresponding to each phase have been developed for fault detection, classification, and similarly four regression modules are developed for fault location estimation depending on the type of fault. The proposed methodology is able to precisely determine the fault, locate it and also recognize the faulty phase(s). Special attention has been paid in determining the high impedance fault (HIF) apart from usual low impedance faults (LIF) while varying the fault parameters such as evolving nature of faults, fault inception angle, power flow angle and change in transmission line parameters etc. Voltage and current measurements recorded at the relay location are utilized. Moreover, the proposed XGBoost algorithm-based approach is able to detect and classify faults in around 1–2 ms time. The absolute error in fault location is around 0–2 km in most of the scenarios. A comparative study clearly states the merits of the proposed scheme.

The Timing of Thigh Muscle Activity Is a Factor Limiting Performance in the Deceleration Phase of the 100-m Dash.

We aimed to examine the timing of electromyography activity of the rectus femoris (RF) and biceps femoris (BF) in both legs, as well as spatiotemporal variables (running speed (RS), step frequency (SF), step length (SL)) between the maximal speed (Max) phase (50-70 m) and the deceleration (Dec) phase (80-100 m) of the 100-m dash. Nine track and field athletes performed the 100-m dash with maximal effort. Spatiotemporal variables of each 10-m section were measured. A portable wireless data logger was attached to the subject's lower back to record electromyographies. We calculated onset/offset timing (%) of RF and BF in both legs using a Teager-Kaiser Energy Operator filter (e.g., ipsilateral leg RF onset is "iRF-onset," contralateral leg BF onset is "cBF-onset") in a running cycle. The decreased RS in the Dec phase (P < 0.001) was due to a decreased SF (P < 0.001). Moreover, iRF-onset (P = 0.002), iRF-offset (P = 0.008), iBF-offset (P = 0.049), and cBF-offset (P = 0.017) in the Dec phase lagged in the running cycle as compared with the Max phase. Furthermore, the time difference between the swing leg RF activity (iRF-onset) and the contact leg BF activity (cBF-onset; "Scissors1") became bigger in the Dec phase (P = 0.041). Significant negative correlations were found between ΔiRF-onset and ΔSF (P = 0.045), and between ΔiBF-offset and ΔSF (P = 0.036). The decreased RS and SF in the Dec phase of the 100-m dash would be the delayed timing of the RF and BF activities in the same leg as well as the disturbed interleg muscular coordination.

Broken Rotor Bar and Rotor Eccentricity Fault Detection in Induction Motors Using a Combination of Discrete Wavelet Transform and TeagerKaiser Energy Operator

In this paper, a hybrid approach is proposed to detect the broken rotor bar and rotor mixed eccentricity faults of three-phase squirrel cage induction motors based on one phase of the stator current signal. The hybrid method is based on a combination of discrete wavelet transform (DWT), Teager–Kaiser energy operator (TKEO) and power spectral density (PSD). At the first stage, DWT is applied on the stator current signal; then TKEO is applied on some of the DWT detail coefficients and finally PSD is used to extract the required feature to detect the aforementioned faults. The proposed method is tested on an experimental test-rig having a 1.5 kW induction motor under various load conditions and three different power supplies: 1) directly supplied by power grid; 2) supplied by a drive system based on scalar control technique; and 3) supplied by a drive system based on direct torque control (DTC) technique. The results show the efficacy of the proposed method in accurate detection of the broken rotor bar and rotor mixed eccentricity faults.

Cardiac Artifact Noise Removal From Sleep EEG Signals Using Hybrid Denoising Model

Sleep is one of the prime natural activities for human well-being in physical, emotional, and mental aspects. The assessment of sleep Electroencephalography (EEG) signals is required to diagnose sleep-related neurological disorders. It is found that sleep EEG signals are extremely vulnerable to highly energetic electrocardiogram (ECG) signals. The intermixing of ECG into EEG, commonly known as cardiac artifacts, might severely affect the sleep EEG data. In order to have artifact-free EEG signal, a hybrid signal denoising methodology which includes empirical wavelet transforms (EWT), adaptive threshold-based nonlinear Teager-Kaiser energy operator (TEO), and customized morphological filter in accompanying with modified ensemble average subtraction (MEAS) is proposed for automatic detection and suppression of cardiac artifact from a single-channel EEG. The efficacy of the proposed methodology presented in the paper has been evaluated over standard public datasets such as CinC Challenge 2014 dataset (synthetic), and MIT-BIH polysomnography data (clinical). It has been observed that the proposed method outperforms other state-of-the-art automated EEG artifact elimination methods in terms of few popular denoising performance indexes such as signal to artifact ratio, percentage root mean square difference, percentage distortion in power spectral density, structural similarity index measure, and execution time. The proposed method is robust, time-efficient, and preserves the majority of EEG data with minimal loss, making it suitable for neuro clinical EEG analysis.

Improved cyclostationary analysis method based on TKEO and its application on the faults diagnosis of induction motors

Cyclostationary analysis has been strongly recognized as an effective demodulation tool in identifying fault features of rotating machinery based on vibration signature analysis. This study improves two current mature cyclostationary approaches, cyclic modulation spectrum (CMS) and fast spectral correlation (Fast-SC), combined with the novel frequency-domain application of Teager Kaiser energy operator (TKEO). They can enhance fault feature identification with the lower computational burden. Firstly, the raw vibration signal is transformed into the time–frequency domain through the short-time Fourier transform (STFT) to realize the conversion of the multi-carrier signal to a multiple signal-carrier signal. Secondly, the TKEO is utilized to enhance the fault feature by taking full advantage of demodulating the mono-component. Finally, the spectral coherence and enhanced envelope spectrum (EES) are calculated to effectively exhibit fault features. The superiority of the proposed methods is successfully validated by the simulation study and diagnosing the broken rotor bar (BRB) and bearing outer race faults of induction motors (IMs) under various operating conditions.

Long short-term memory (LSTM) recurrent neural network for muscle activity detection

BackgroundThe accurate temporal analysis of muscle activation is of great interest in many research areas, spanning from neurorobotic systems to the assessment of altered locomotion patterns in orthopedic and neurological patients and the monitoring of their motor rehabilitation. The performance of the existing muscle activity detectors is strongly affected by both the SNR of the surface electromyography (sEMG) signals and the set of features used to detect the activation intervals. This work aims at introducing and validating a powerful approach to detect muscle activation intervals from sEMG signals, based on long short-term memory (LSTM) recurrent neural networks.MethodsFirst, the applicability of the proposed LSTM-based muscle activity detector (LSTM-MAD) is studied through simulated sEMG signals, comparing the LSTM-MAD performance against other two widely used approaches, i.e., the standard approach based on Teager–Kaiser Energy Operator (TKEO) and the traditional approach, used in clinical gait analysis, based on a double-threshold statistical detector (Stat). Second, the effect of the Signal-to-Noise Ratio (SNR) on the performance of the LSTM-MAD is assessed considering simulated signals with nine different SNR values. Finally, the newly introduced approach is validated on real sEMG signals, acquired during both physiological and pathological gait. Electromyography recordings from a total of 20 subjects (8 healthy individuals, 6 orthopedic patients, and 6 neurological patients) were included in the analysis.ResultsThe proposed algorithm overcomes the main limitations of the other tested approaches and it works directly on sEMG signals, without the need for background-noise and SNR estimation (as in Stat). Results demonstrate that LSTM-MAD outperforms the other approaches, revealing higher values of F1-score (F1-score > 0.91) and Jaccard similarity index (Jaccard > 0.85), and lower values of onset/offset bias (average absolute bias < 6 ms), both on simulated and real sEMG signals. Moreover, the advantages of using the LSTM-MAD algorithm are particularly evident for signals featuring a low to medium SNR.ConclusionsThe presented approach LSTM-MAD revealed excellent performances against TKEO and Stat. The validation carried out both on simulated and real signals, considering normal as well as pathological motor function during locomotion, demonstrated that it can be considered a powerful tool in the accurate and effective recognition/distinction of muscle activity from background noise in sEMG signals.

Detection of spontaneous seizures in EEGs in multiple experimental mouse models of epilepsy

Objective. Electroencephalography (EEG) is a key tool for non-invasive recording of brain activity and the diagnosis of epilepsy. EEG monitoring is also widely employed in rodent models to track epilepsy development and evaluate experimental therapies and interventions. Whereas automated seizure detection algorithms have been developed for clinical EEG, preclinical versions face challenges of inter-model differences and lack of EEG standardization, leaving researchers relying on time-consuming visual annotation of signals. Approach. In this study, a machine learning-based seizure detection approach, ‘Epi-AI’, which can semi-automate EEG analysis in multiple mouse models of epilepsy was developed. Twenty-six mice with a total EEG recording duration of 6451 h were used to develop and test the Epi-AI approach. EEG recordings were obtained from two mouse models of kainic acid-induced epilepsy (Models I and III), a genetic model of Dravet syndrome (Model II) and a pilocarpine mouse model of epilepsy (Model IV). The Epi-AI algorithm was compared against two threshold-based approaches for seizure detection, a local Teager-Kaiser energy operator (TKEO) approach and a global Teager-Kaiser energy operator-discrete wavelet transform (TKEO-DWT) combination approach. Main results. Epi-AI demonstrated a superior sensitivity, 91.4%–98.8%, and specificity, 93.1%–98.8%, in Models I–III, to both of the threshold-based approaches which performed well on individual mouse models but did not generalise well across models. The performance of the TKEO approach in Models I–III ranged from 66.9%–91.3% sensitivity and 60.8%–97.5% specificity to detect spontaneous seizures when compared with expert annotations. The sensitivity and specificity of the TKEO-DWT approach were marginally better than the TKEO approach in Models I–III at 73.2%–80.1% and 75.8%–98.1%, respectively. When tested on EEG from Model IV which was not used in developing the Epi-AI approach, Epi-AI was able to identify seizures with 76.3% sensitivity and 98.1% specificity. Significance. Epi-AI has the potential to provide fast, objective and reproducible semi-automated analysis of multiple types of seizure in long-duration EEG recordings in rodents.

Fast Power System Event Identification Using Enhanced LSTM Network With Renewable Energy Integration

Accurate and fast event identification in power systems is critical for taking timely controls to avoid instability. In this paper, a synchrophasor measurement-based fast and robust event identification method is proposed considering different penetration levels of renewable energy. A difference Teager-Kaiser energy operator (dTKEO)-based algorithm is first proposed to improve multiple-events detection accuracy. Then, feature extractions via the integrated additive angular margin (AAM) loss and the long short-term memory (LSTM) network are developed. This allows us to deal with intra-class similarity and inter-class variance of events when high penetration renewable energy occurs. With the extracted features, a multi-stage weighted summing (MSWS) loss-based criterion is developed for adaptive data window determination and fast event pre-classification. Finally, the re-identification model based on feature similarity is established to identify unknown events, a challenge for existing machine learning algorithms. Simulation results on the IEEE 39-bus, Kundur 2-area, and an actual large-scale power grid system are used to demonstrate the advantages of the proposed method over others.

Fault diagnosis in a hydraulic directional valve using a two-stage multi-sensor information fusion

As it is usually operating in bad working conditions and subjected to the severe interference from diverse paths, internal faults of the hydraulic valve are difficult to be detected using conventional hydraulic testing technology (such as relying on pressure sensors or flow sensors). Moreover, the information collected from a single sensor may not provide accurate diagnostic evidence or a complete description on faults of hydraulic valves, even though employing intelligent fault diagnosis methods. Therefore, a two-stage multi-sensor information fusion method is proposed, including the fault feature fusion and the decision-making information fusion. The aim is to realize the diagnosis on internal faults of hydraulic directional valves using the vibration signal analysis method instead of conventional hydraulic testing ones. The method is mainly divided into three steps. First, the noise reduction of the vibration information collected by multiple acceleration sensors is done using ensemble empirical mode decomposition (EEMD) and Teager-Kaiser energy operator (TEO), so that fault features are more obvious. Then, multi-class fault features including the severity and the location of the wear are extracted from the preprocessing signals to form the original feature set. Second, combined with feature ranking and subset selection based on euclidean distance (FRSSED) and maximum relevance minimum redundancy (mRMR) feature selection method, the statistical features extracted from multiple sensor signals are optimized to form the optimal feature subset. This is the first-level information fusion concerned with fault feature information fusion. In the third step, based on Dempster-Shafer (DS) evidence theory and convolutional neural network (CNN), decision-making information is fused (called second-level information fusion) to obtain the final diagnosis results. A hydraulic test bench is built to test different failure valves. Experimental results indicate that this method is effective in extracting the fault features from multi-sensor signals and detecting the fault states including severity and location of internal wear of the hydraulic valve.

Detection of hidden reservoirs under strong shielding based on bi‐dimensional empirical mode decomposition and the Teager–Kaiser operator

ABSTRACTIn this paper, we propose a method for revealing hidden reservoirs that are shielded by strong amplitudes. The bi‐dimensional empirical mode decomposition algorithm is used to decompose pre‐stack seismic data into several localized components, which generated from different discontinuities in the subsurface elastic properties. The two‐dimensional Teager–Kaiser energy operator process is applied to the first component, which includes a strong signal, to further locate the strong event. According to the located results, an energy weight matrix is established. By weighted summation of all the components, the strong event is suppressed, and the hidden reservoir becomes more prominent. Tests on a synthetic data and field data from Daniudi confirm that this method can separate strong signals from weaker responses and efficiently reveal shielded reservoirs.

Electromyographic activity of the lower limb in runners with anterior knee pain while running

Anterior knee pain (AKP) is a common musculoskeletal condition that affects runner athletes of different ages. Lower level and delayed onset electromyographic activity (EMG) of the vastus medialis oblique muscle (VMO) and the vastus lateralis (VL) have been identified in patients with AKP. Recently, it has also been suggested that a delay or decrease in the level of gluteus medius (GM) activation may influence the biomechanics of patellofemoral joint. Thus, this study aimed to compare the EMG patterns of VMO, VL, and GM during treadmill running in recreational runners with AKP. Twelve men with AKP (PG) evaluated by the scale (Scoring of Patellofemoral Disorders) and 20 healthy subjects (CG) participated in this study. The EMG of the muscles was collected during 15 s of running at a speed of 11 km/h. The running cycle was captured by four infrared cameras and 3D visual analysis software. The activation level was measured by the rectified EMG integral and the onset of contraction by the Teager-Kaiser energy operator. The VMO and GM muscles showed significantly shorter pre-activation times and contraction times in the group of runners with pain (p < 0.05). Before the start of the running cycle, only the VMO muscle showed differences, with a lower level for the AKP group. In the other phases of the cycle, there were no significant differences. The results suggest that both the VMO and GM have delay and activation deficits, without changes in the level of activation in patients with AKP, which can contribute to patellar misalignment and consequently to the appearance of pain.

Minimum Mapping from EMG Signals at Human Elbow and Shoulder Movements into Two DoF Upper-Limb Robot with Machine Learning

This research focuses on the minimum process of classifying three upper arm movements (elbow extension, shoulder extension, combined shoulder and elbow extension) of humans with three electromyography (EMG) signals, to control a 2-degrees of freedom (DoF) robotic arm. The proposed minimum process consists of four parts: time divisions of data, Teager–Kaiser energy operator (TKEO), the conventional EMG feature extraction (i.e., the mean absolute value (MAV), zero crossings (ZC), slope-sign changes (SSC), and waveform length (WL)), and eight major machine learning models (i.e., decision tree (medium), decision tree (fine), k-Nearest Neighbor (KNN) (weighted KNN, KNN (fine), Support Vector Machine (SVM) (cubic and fine Gaussian SVM), Ensemble (bagged trees and subspace KNN). Then, we compare and investigate 48 classification models (i.e., 47 models are proposed, and 1 model is the conventional) based on five healthy subjects. The results showed that all the classification models achieved accuracies ranging between 74–98%, and the processing speed is below 40 ms and indicated acceptable controller delay for robotic arm control. Moreover, we confirmed that the classification model with no time division, with TKEO, and with ensemble (subspace KNN) had the best performance in accuracy rates at 96.67, recall rates at 99.66, and precision rates at 96.99. In short, the combination of the proposed TKEO and ensemble (subspace KNN) plays an important role to achieve the EMG classification.

Onset detection in surface electromyographic signals across isometric explosive and ramped contractions: a comparison of computer-based methods

Objective. Accurate identification of surface electromyography (EMG) muscle onset is vital when examining short temporal parameters such as electromechanical delay. The visual method is considered the ‘gold standard’ in onset detection. Automatic detection methods are commonly employed to increase objectivity and reduce analysis time, but it is unclear if they are sensitive enough to accurately detect EMG onset when relating them to short-duration motor events. Approach. This study aimed to determine: (1) if automatic detection methods could be used interchangeably with visual methods in detecting EMG onsets (2) if the Teager–Kaiser energy operator (TKEO) as a conditioning step would improve the accuracy of popular EMG onset detection methods. The accuracy of three automatic onset detection methods: approximated generalized likelihood ratio (AGLR), TKEO, and threshold-based method were examined against the visual method. EMG signals from fast, explosive, and slow, ramped isometric plantarflexor contractions were evaluated using each technique. Main results. For fast, explosive contractions, the TKEO was the best-performing automatic detection method, with a low bias level (4.7 ± 5.6 ms) and excellent intraclass correlation coefficient (ICC) of 0.993, however with wide limits of agreement (LoA) (−6.2 to +15.7 ms). For slow, ramped contractions, the AGLR with TKEO conditioning was the best-performing automatic detection method with the smallest bias (11.3 ± 32.9 ms) and excellent ICC (0.983) but produced wide LoA (−53.2 to +75.8 ms). For visual detection, the inclusion of TKEO conditioning improved inter-rater and intra-rater reliability across contraction types compared with visual detection without TKEO conditioning. Significance. In conclusion, the examined automatic detection methods are not sensitive enough to be applied when relating EMG onset to a motor event of short duration. To attain the accuracy needed, visual detection is recommended. The inclusion of TKEO as a conditioning step before visual detection of EMG onsets is recommended to improve visual detection reliability.

Application of Teager-Kaiser's Instantaneous Frequency for Detection of Delamination in FRP Composite Materials.

Composite materials are widely used in many engineering applications and fields of technology. One of the main defects, which occur in fiber-reinforced composite materials, is delamination. It manifests itself in the separation of layers of material and the damaged structure once subjected to mechanical loads degrades further. Delamination results in lower stiffness and the decrease of structure’s carry load capability. Its early detection is one of the tasks of non-invasive structural health monitoring of layered composite materials. This publication discusses a new method for delamination detection in fiber-reinforced composite materials. The approach is based on analysis of energy signal, calculated with Teager–Kaiser energy operator, and comparison of change of the weighted instantaneous frequency for measurement points located in- and outside of delamination area. First, applicability of the developed method was tested using simple models of vibration signals, reflecting considered phenomena. Next, the authors’ weighted instantaneous frequency was applied for detection of deamination using signals obtained from FEM simulated response of the cantilever beam. Finally, the methods effectiveness were tested involving real experimental signals collected by the laser Doppler vibrometer (LVD) sensor measuring vibrations of the delaminated glass-epoxy specimens.