Frequency based EMG power spectrum analysis of Salat associated muscle contraction

Khanam, Farzana; Ahmad, Mohiuddin

doi:10.1109/ceee.2015.7428245

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…Thus, meaningful features must be extracted from the signal before they can be used for muscle analysis [25]. EMG signals can be quantified in both time and frequency domains, and the analysis results often require one or the other for the best utility, such as determining muscle fatigue [26][27][28][29]. Table 1 lists the most common time and frequency domain measures.…”

Section: Emg Signal Analysismentioning

confidence: 99%

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Factors affecting real‐time evaluation of muscle function in smart rehab systems

Joe,

Kim,

Lee

et al. 2023

ETRI Journal

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Advancements in remote medical technologies and smart devices have led to expectations of contactless rehabilitation. Conventionally, rehabilitation requires clinicians to perform routine muscle function assessments with patients. However, assessment results are difficult to cross‐reference owing to the lack of a gold standard. Thus, the application of remote smart rehabilitation systems is significantly hindered. This study analyzes the factors affecting the real‐time evaluation of muscle function based on biometric sensor data so that we can provide a basis for a remote system. We acquired real clinical stroke patient data to identify the meaningful features associated with normal and abnormal musculature. We provide a system based on these emerging features that assesses muscle functionality in real time via streamed biometric signal data. A system view based on the amount of data, data processing speed, and feature proportions is provided to support the production of a rudimentary remote smart rehabilitation system.

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Section: Emg Signal Analysismentioning

confidence: 99%

“…In this study, the fast Fourier transform (FFT) [26] was used to convert a signal from the time domain into the frequency domain; it is the most widely used method for doing this for EMG signals.…”

Section: Muscle Function Assessment Schemesmentioning

confidence: 99%

Factors affecting real‐time evaluation of muscle function in smart rehab systems

Joe,

Kim,

Lee

et al. 2023

ETRI Journal

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“…Frequencydomain or spectral-domain features are commonly used to convert EMG signals in time-domain to frequency-domain and also to assess muscle fatigue. The Fourier transform of the autocorrelation function of the EMG signal is used to provide Power Spectral (PS) or Power Spectral Density (PSD) [22].…”

Section: Musclementioning

confidence: 99%

Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram (EMG Parameter ) Monitoring Signals

Fauzi

Yulianto

Irianto

et al. 2022

Indones.J.electronic.electromed.med.inf

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Physical activity is an activity of body movement by utilizing skeletal muscles that is carried out daily. One form of physical activity is an exercise that aims to improve health and fitness. Parameters related to health and fitness are heart and muscle activity. Strong and prolonged muscle contractions result in muscle fatigue. To measure muscle fatigue, the authors used electromyographic (EMG) signals through monitoring changes in muscle electrical activity. This study aims to make a tool to detect the effect of muscle fatigue on cardiac signals on physical activity. This research method uses Fast Fourier Transform (FFT) with one group pre-test-post-test research design. The independent variable is the EMG signal when doing plank activities, while the dependent variable is the result of monitoring the EMG signal. To get more detailed measurement results, the authors use MPF, MDF and MNF and perform a T-test. The test results showed a significant value (pValue <0.05) in the pre-test and post-test. The Pearson correlation test got a value of 0.628 which indicates there is a strong relationship between exercise frequency and plank duration. When the respondent experiences muscle fatigue, the heart signal is affected by noise movement artifacts that appear when doing the plank. It is concluded that the tools in this study can be used properly. To overcome noise in the EMG signal, it is recommended to use dry electrodes and high-quality components. To improve the ability to transmit data, it is recommended to use a Raspberry microcontroller.

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“…A FMEAN (GARCIA;VIEIRA, 2019;GROSS et al, 2000;GRUSS et al, 2015;KHANAM;AHMAD, 2016;PHINYOMARK et al, 2010) extrai a frequência média do espectro de frequências, e é dada pela Equação (9):…”

Section: -Características Relacionadas Com a Frequênciaunclassified

Discriminação do tremor em repouso do punho entre indivíduos com e sem a doença de parkinson por meio de sensores inerciais e classificadores

Peres¹

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Frequency based EMG power spectrum analysis of Salat associated muscle contraction

Cited by 13 publications

References 7 publications

Factors affecting real‐time evaluation of muscle function in smart rehab systems

Factors affecting real‐time evaluation of muscle function in smart rehab systems

Effect of Muscle Fatigue on Heart Signal on Physical Activity with Electromyogram and Electrocardiogram (EMG Parameter ) Monitoring Signals

Discriminação do tremor em repouso do punho entre indivíduos com e sem a doença de parkinson por meio de sensores inerciais e classificadores

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