Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles

Chowdhury, S.; Nimbarte, Ashish D.; Jaridi, Majid; Creese, Robert C.

doi:10.1016/j.jelekin.2013.05.001

Cited by 65 publications

(37 citation statements)

References 31 publications

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“…Chowdhury et al (2013) used Discrete Wavelet Transform (DWT) to assess the neck and shoulder muscle fatigue under dynamic repetitive conditions. Their findings state that the muscle fatigue produced a spectral change in the lower frequency band of 12-23 Hz of the SEMG signals collected from the muscles.…”

Section: Data Collectionmentioning

confidence: 99%

Gait analysis parameters of healthy human subjects with asymmetric loads

Berceanu

Marghitu

Gudavalli

et al. 2015

Computer Methods in Biomechanics and Biomedical Engineering

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This article focuses on the analysis of gait parameters, ground reaction forces (GRF), and motion signals, for the various asymmetric loads carried by healthy human subjects during walking. Eight asymptomatic human volunteers were enrolled in this study. They were asked to walk, at self-selected pace, with various weights ranging from 0 to 11.33 kg (25 lbs) in 2.26 kg (5 lbs) increments, in one hand on a wooden area equipped with a force platform. Moreover, motion data were recorded from lumbar L1 vertebrae at a frequency of 120 Hz. Three trials of data have been recorded for each subject. In order to quantify the effect of increasing loads on the GRF we define the compression area, restitution area, and coefficient of restitution (COR) for GRF curves. We observe an increase in the compression area with respect to the load and almost constant values for the COR. For motion signals analysis we employ wavelet theory. The signals obtained from the lumbar L1 sensor of the spine vertebrae show a decrease in the wavelet detail energy, for the levels 3, 4, and 5, with respect to increasing loads.

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Section: Data Collectionmentioning

confidence: 99%

Gait analysis parameters of healthy human subjects with asymmetric loads

Berceanu

Marghitu

Gudavalli

et al. 2015

Computer Methods in Biomechanics and Biomedical Engineering

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“…Electromyogram or better known as EMG is a collective electrical signals acquired from the muscles when the muscles perform contraction controlled by the nervous system [5]. The sEMG signal is a weak electrical potential recorded by electrodes from the skin.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many studies were done to analyse muscle fatigue in different muscle groups [1][2][3][4][5][6][7]. Artificial neural network (ANN), Support Vector Machine (SVM), and K-nearest neighbour (K-NN) are among the promising techniques in predicting muscle fatigue [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Sharawardi

Choo

Shao

et al. 2014

2014 4th World Congress on Information and Communication Technologies (WICT 2014)

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Surface electromyogram (sEMG) signal is commonly used for muscle fatigue analysis in clinical rehabilitation studies. Prediction results based on sEMG signals are promising because muscle contradiction can be easily characterized using sEMG signals. However, the prediction results usually deteriorate significantly when noise exist during data acquisition. Noise happens due to many factors ranging from hardware, software to procedure flaws. This investigation is aimed to assess the performance of the Least Square SVM model in predicting muscle fatigue using single channel sEMG signal. The root mean square, median frequency, and mean frequency features were extracted from two sets of raw sEMG signals captured at the multifidus (for low back pain) and flexor carpi radialis (for forearm muscle fatigue) muscles. The proposed LS-SVM technique were used to build the prediction rule-base separately for both the datasets. The implementation, testing and verification were performed in Matlab environment. The k-nearest neighbour and artificial neural network were used as the benchmarking techniques in results comparison and analysis. LS-SVM technique is proven good against the benchmarking techniques on classification accuracy and area under ROC curve. The ANOVA and Tukey HSD post hoc test were used to further validate the significant of the comparison results on both accuracy and AUC measurements.

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“…For instance, the vastus lateralis, vastus medialis and rectus femoris EMG signals are measured and analysed during cycling, walking and running activities [42, 9 and 43]. Investigations have also broaden to real life scenarios as muscle fatigue has also been evaluated in elderly people [44 and 45], athletes [10 and 16], musculoskeletal disorders [46] and for ergonomic purposes [47].…”

Section: Time-frequency Analysismentioning

confidence: 99%

Future direction of the electromyography based method to evaluate muscle fatigue

Jamaluddin

Ahmad

Noor

et al. 2014

2014 4th International Conference on Engineering Technology and Technopreneuship (ICE2T)

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Fatigue detection through electromyography analysis has been in use since 1912 when it was discovered by H.Piper. Until now, there has been much research on muscle fatigue through electromyography, both invasively and noninvasively. There are evolutions of the electromyography based approach to detect fatigue in various aspects including methodology, analysis, application and the evaluation of fatigue itself. This paper intends to provide a brief review of previous research as this will help beginning researcher to find further direction in this research area.

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Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles

Cited by 65 publications

References 31 publications

Gait analysis parameters of healthy human subjects with asymmetric loads

Gait analysis parameters of healthy human subjects with asymmetric loads

Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Future direction of the electromyography based method to evaluate muscle fatigue

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