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Surface Electromyography (sEMG) activity of the biceps muscle was recorded from ten subjects performing isometric contraction until fatigue. The signals were segmented into two parts (NonFatigue and Transition-to-Fatigue). The novel feature was extracted from two established muscle fatigue detection features (Instantaneous Median Frequency and Total Band Power of the signal). The proposed name for...
This paper bispectrum is used to classify human arm movements and control a robotic arm based on upper limb's surface electromyogram signals (sEMG). We use bispectrum based on third-order cumulant to parameterize sEMG signals and classify elbow flexion and extension, forearm pronation and supination, and rest states by an artificial neural network (ANN). Finally, a robotic manipulator is controlled...
There is an urgent need for a simple yet robust system to identify natural hand actions and gestures for controlling prostheses and other computer-assisted devices. Surface electromyogram (SEMG) is a non-invasive measure of the muscle activities but is not reliable because there are a multiple simultaneously active muscles. This study proposes the use of independent component analysis (ICA) for SEMG...
The objective of this study was to evaluate the usefulness of AM-FM features extracted from surface electromyographic (SEMG) signals for the assessment of neuromuscular disorders at different force levels. SEMG signals were recorded from a total of 40 subjects, 20 normal and 20 patients, at 10%, 30%, 50%, 70% and 100% of maximum voluntary contraction (MVC), from the biceps brachii muscle. From the...
This paper introduces an approach to obtain the feature vectors of surface electromyography (sEMG) signal based on Hilbert Huang transform (HHT). An adaptive segmentation method that could effectively select appropriate intrinsic mode function (IMF) is proposed. With the features gathered by using the energy of one channel signal, we also provide an optimized strategy based on experiments and experiences...
The paper aims to identify speech using the facial muscle activity without the audio signals. The paper presents an effective technique that measures the relative muscle activity of the articulatory muscles. Five English vowels were used as recognition variables. This paper reports using moving root mean square (RMS) of surface electromyogram (SEMG) of four facial muscles to segment the signal and...
To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting...
The paper aims to identify speech using the facial muscle activity without the audio signals. The paper presents an effective technique that measures the relative muscle activity of the articulatory muscles. Five English vowels were used as recognition variables. This paper reports using moving root mean square (RMS) of surface electromyogram (SEMG) of four facial muscles to segment the signal and...
To date various signal processing techniques have been applied to surface electromyography (SEMG) for feature extraction and signal classification. Compared with traditional analysis methods which have been used in previous application, continuous wavelet transform (CWT) enhances the SEMG features more effectively. This paper presents methods of analysing SEMG signals using CWT and LabVIEW for extracting...
The surface electromyographic (SEMG) signal, which is produced by neural and muscular systems, is a complicated bioelectric signal recorded from skin surface using electrodes. It is very helpful for doctors to analyse the illness of patients. In the paper, four channel SEMG signals from four muscles (palmaris longus, brachioradialis, flexor carpi ulnaris, biceps brachii) are analyzed with wavelet...
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