Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals

Venugopal, G.; Navaneethakrishna, M.; Swaminathan, Ramakrishnan

doi:10.1016/j.eswa.2013.11.009

Cited by 145 publications

(84 citation statements)

References 32 publications

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“…Due to the sampling frequency limitation, we choose threeband signal decomposition (<10 Hz, 10-50 Hz, and >50 Hz). We name the average instantaneous amplitude of subband sEMG signal from the three bands as AIE Lower frequency sEMG signals have been found more prominent in identifying muscle fatigue than the higher frequency bands (Petrofsky et al [32] and Venugopal et al [34]). The following exploration of fatiguing effects hereby only involves the lower band of sEMG to avoid other irrelevant factors.…”

Section: Muscle Fatigue Assessment From Semgmentioning

confidence: 99%

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Wang

et al. 2018

Complexity

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To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists.

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Section: Muscle Fatigue Assessment From Semgmentioning

confidence: 99%

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Wang

et al. 2018

Complexity

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“…This corresponds to a directional pattern counter with a specific distance d and angle θ between neighbouring pixel pairs for grayscale images [7]. 1]. The features such as contrast, correlation, energy and homogeneity are extracted and the formula is given below:…”

Section: Glcm Featuresmentioning

confidence: 99%

“…It arises due to sustained or intense contraction, Parkinson's disease, carcinoma, endocrine disturbances, malnutrition and immobilization [1]. Repeated fatigue may lead to irreversible impairment of muscles.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of muscle fatigue conditions using time-frequency images and GLCM features

Karthick

Navaneethakrishna

Punitha

et al. 2016

Current Directions in Biomedical Engineering

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Abstract:In this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture representation of the timefrequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of these signals are considered as the non-fatigue and fatigue zones, respectively. These signals are preprocessed and the time-frequency spectrum is computed using short time fourier transform (STFT). Gray-Level Co-occurrence Matrix (GLCM) is extracted from low (15-45 Hz), medium (46-95 Hz) and high (96-150 Hz) frequency bands of the time-frequency images. Further, the features such as contrast, correlation, energy and homogeneity are calculated from the resultant matrices. The results show that the high frequency band based features are able to differentiate non-fatigue and fatigue conditions. The features such as correlation, contrast and homogeneity extracted at angles 0°, 45°, 90°, and 135°are found to be distinct with high statistical significance (p < 0.0001). Hence, this framework can be used for analysis of neuromuscular disorders.

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“…Firstly, DHWPT (Discrete Harmonic Wavelet Packet Transform) was used to extract the relative energy of sEMG signals in each frequency band, and secondly, a GA choose the optimal features for reducing feature dimensionality. In a study aiming at differentiating Fatigue and Non-Fatigue segments of the sEMG signal using multiple time window (MTW) features, a GA and information based ranking selects the prominent features [17]. The feature reduction was 45% for the GA compared to 36% for the information based ranking.…”

Section: Introductionmentioning

confidence: 99%

Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

2015

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Surface electromyographic (sEMG) activity of the biceps muscle was recorded from 13 subjects. Data was recorded while subjects performed dynamic contraction until fatigue and the signals were segmented into two parts (Non-Fatigue and Fatigue). An evolutionary algorithm was used to determine the elbow angles that best separate (using Davies-Bouldin Index, DBI) both Non-Fatigue and Fatigue segments of the sEMG signal. Establishing the optimal elbow angle for feature extraction used in the evolutionary process was based on 70% of the conducted sEMG trials. After completing 26 independent evolution runs, the best run containing the optimal elbow angles for separation (Non-Fatigue and Fatigue) was selected and then tested on the remaining 30% of the data to measure the classification performance. Testing the performance of the optimal angle was undertaken on nine features extracted from each of the two classes (Non-Fatigue and Fatigue) to quantify the performance. Results showed that the optimal elbow angles can be used for fatigue classification, showing 87.90% highest correct classification for one of the features and on average of all eight features (including worst performing features) giving 78.45%.

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Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals

Cited by 145 publications

References 32 publications

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Analysis of muscle fatigue conditions using time-frequency images and GLCM features

Optimal Elbow Angle for Extracting sEMG Signals During Fatiguing Dynamic Contraction

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