Arm EMG Wavelet-Based Denoising System

Gradolewski, Dawid; Tojza, Piotr; Jaworski, Jacek; Ambroziak, Dominik; Redlarski, Grzegorz; Krawczuk, Marek

doi:10.1007/978-3-319-10990-9_26

“…In biomedical signals, wavelet transforms have also been suggested for signal compression [34], cardiac pattern recognition [35], EMG classification and decodification [34,36], feature detection and extraction for ECGs [35] and PPGs [37], and epilepsy diagnosis [38]. Finally, in this chapter, we detail two potential usage scenarios for wavelet techniques, such as gait analysis and arm swing analysis.…”

Section: Wavelet In Biomedical Applicationsmentioning

confidence: 99%

Using Wavelets for Gait and Arm Swing Analysis

Castaño-Pino¹,

Navarro²,

Muñoz³

et al. 2019

Wavelet Transform and Complexity

View full text Add to dashboard Cite

The human walking pattern can be affected by different factors such as accidents, transplants, or diseases, like Parkinson's disease, which affects motor and mental functions. In motor terms, this disease can generate alterations such as tremors, festination, rigidity, unbalance, slowness, and freezing of gait. Additionally, it is estimated that for the year 2040, the number of people with Parkinson's in the world will be between 12.9 and 14.2 million people. These alarming figures make Parkinson's disease an important focus of attention. In this chapter, we present contributions that suggest wavelet techniques as a useful tool to perform a gait and arm swing analysis; this represents an important approximation that can contribute to describe and differentiate people with Parkinson's disease in early stages of the disease.

show abstract

“…In the EEMD process, each individual trial may produce noisy results, as each decomposition step involves both the signal and added white noise. On the other hand, when the EMD method is applied to a white noise [13,18,19], it acts as an adaptive dyadic filter bank. As a result, the Fourier spectra of different IMFs would collapse onto a single spectrum along the axis of frequency [20].…”

Section: Ensemble Empirical Mode Decomposition Methodsmentioning

confidence: 99%

“…In our analysis, we have adopted the fGn model for various H values developed by Flandrin et al [13,18,19] is adopted here. fGn is generalization of white noise, and the statistical properties of fGn are controlled by the parameter H, and the H values range from 0.1 to 0.9, and for each value of H, J = 5000 independent sample paths of fGn were generated through the Wood and Chan [22] algorithm.…”

Section: Denoising Algorithmmentioning

confidence: 99%

A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Zhang¹,

Soangra

²

,

Lockhart

³

2020

Sci

View full text Add to dashboard Cite

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

show abstract

“…In our analysis, we have adopted the fGn model for various H values developed by Flandrin et al [13,18,19] is adopted here. fGn is generalization of white noise, and the statistical properties of fGn are controlled by the parameter H, and the H values range from 0.1 to 0.9, and for each value of H, J = 5000 independent sample paths of fGn were generated through the Wood and Chan [22] algorithm.…”

Section: Denoising Algorithmmentioning

confidence: 99%

A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Zhang¹,

Soangra²,

Lockhart³

2020

Sci

2

0

View full text Add to dashboard Cite

One of the most basic pieces of information gained from dynamic electromyography is accurately defining muscle action and phase timing within the gait cycle. The human gait relies on selective timing and the intensity of appropriate muscle activations for stability, loading, and progression over the supporting foot during stance, and further to advance the limb in the swing phase. A common clinical practice is utilizing a low-pass filter to denoise integrated electromyogram (EMG) signals and to determine onset and cessation events using a predefined threshold. However, the accuracy of the defining period of significant muscle activations via EMG varies with the temporal shift involved in filtering the signals; thus, the low-pass filtering method with a fixed order and cut-off frequency will introduce a time delay depending on the frequency of the signal. In order to precisely identify muscle activation and to determine the onset and cessation times of the muscles, we have explored here onset and cessation epochs with denoised EMG signals using different filter banks: the wavelet method, empirical mode decomposition (EMD) method, and ensemble empirical mode decomposition (EEMD) method. In this study, gastrocnemius muscle onset and cessation were determined in sixteen participants within two different age groups and under two different walking conditions. Low-pass filtering of integrated EMG (iEMG) signals resulted in premature onset (28% stance duration) in younger and delayed onset (38% stance duration) in older participants, showing the time-delay problem involved in this filtering method. Comparatively, the wavelet denoising approach detected onset for normal walking events most precisely, whereas the EEMD method showed the smallest onset deviation. In addition, EEMD denoised signals could further detect pre-activation onsets during a fast walking condition. A comprehensive comparison is discussed on denoising EMG signals using EMD, EEMD, and wavelet denoising in order to accurately define an onset of muscle under different walking conditions.

show abstract

Arm EMG Wavelet-Based Denoising System

Cited by 17 publications

References 11 publications

Using Wavelets for Gait and Arm Swing Analysis

Using Wavelets for Gait and Arm Swing Analysis

A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

A Comparison of Denoising Methods in Onset Determination in Medial Gastrocnemius Muscle Activations during Stance

Contact Info

Product

Resources

About