Electromyography signal analysis using spectrogram

Zawawi, Tengku Nor Shuhada Tengku; Abdullah, Abdul Rahim; Shair, Ezreen Farina; Isa, Halim; Rawaida, O.

doi:10.1109/scored.2013.7002599

Cited by 18 publications

(6 citation statements)

References 11 publications

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“…EMG spectrogram images obtained by extracting features using each window length are shown in Fig. 7 [29]. In addition, the stride was set to 2.…”

Section: User Recognition System Using 2d Spectrogram Image Based On ...mentioning

confidence: 99%

User Recognition System Based on Spectrogram Image Conversion Using EMG Signals

Kim¹,

Choi²,

Kim³

et al. 2022

Computers, Materials &Amp; Continua

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Recently, user recognition methods to authenticate personal identity has attracted significant attention especially with increased availability of various internet of things (IoT) services through fifth-generation technology (5G) based mobile devices. The EMG signals generated inside the body with unique individual characteristics are being studied as a part of nextgeneration user recognition methods. However, there is a limitation when applying EMG signals to user recognition systems as the same operation needs to be repeated while maintaining a constant strength of muscle over time. Hence, it is necessary to conduct research on multidimensional feature transformation that includes changes in frequency features over time. In this paper, we propose a user recognition system that applies EMG signals to the short-time fourier transform (STFT), and converts the signals into EMG spectrogram images while adjusting the time-frequency resolution to extract multidimensional features. The proposed system is composed of a data pre-processing and normalization process, spectrogram image conversion process, and final classification process. The experimental results revealed that the proposed EMG spectrogram image-based user recognition system has a 95.4% accuracy performance, which is 13% higher than the EMG signal-based system. Such a user recognition accuracy improvement was achieved by using multidimensional features, in the time-frequency domain.

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“…EMG spectrogram images obtained by extracting features using each window length are shown in Fig. 7 [29]. In addition, the stride was set to 2.…”

Section: User Recognition System Using 2d Spectrogram Image Based On ...mentioning

confidence: 99%

User Recognition System Based on Spectrogram Image Conversion Using EMG Signals

Kim¹,

Choi²,

Kim³

et al. 2022

Computers, Materials &Amp; Continua

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“…The fundamental frequency of the battery occurs at the highest magnitude of the ST. The instantaneous direct current voltage (V DC (t)) [42] is expressed as…”

Section: E Instantaneous Direct Current Voltagementioning

confidence: 99%

Lead Acid Battery Analysis using S-Transform

Basir¹,

Abdullah²,

Selamat³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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This paper proposes a new signal processing technique using time-frequency distribution (TFD), namely S-transform (ST) for battery parameters estimation. Compared to other TFDs such as short time Fourier transform (STFT) and wavelet transform (WT), ST technique offers more promising results in a low frequency application analysis, especially battery. The results of the ST are the parameters of instantaneous means square voltage (V RMS (t)), instantaneous direct current voltage (V DC (t)) and instantaneous alternating current voltage (V AC (t)) extracted from the time-frequency representation (TFR). Simulation through MATLAB has been conducted using equivalent circuit model (ECM), using 12 V lead acid (LA) battery with capacities from 1.0 Ah to 10.0 Ah. For the battery model, charging/discharging signal has been used to estimate the battery parameters from the ST technique to determine battery characteristics. From the signal characteristics of battery capacity versus V AC (t) obtained, new equation is proposed based on the curve fitting tool. The advantage of this technique embraces a better capability in estimating battery parameters at low frequency component, resulting in better frequency and time resolutions compared to previous TFDs.

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“…STFT is a commonly used method for spectrogram image formation [2]. Each signal is divided into smaller segments and subjected to discrete fourier transform (DFT).…”

Section: Stft Spectrogrammentioning

confidence: 99%

“…Jac Fredo and S. Ramakrishnan: Indian Institute of Technology, Chennai, India-600036 to analyse fatigue conditions for the clinical diagnosis of muscle disorders. Surface electromyography (sEMG) is a non-invasive technique which is commonly used to analyse muscle fatigue [2].…”

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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Electromyography signal analysis using spectrogram

Cited by 18 publications

References 11 publications

User Recognition System Based on Spectrogram Image Conversion Using EMG Signals

User Recognition System Based on Spectrogram Image Conversion Using EMG Signals

Lead Acid Battery Analysis using S-Transform

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

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