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.
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Periodic Signal Denoising: An Analysis-Synthesis Framework Based on Ramanujan Filter Banks and Dictionaries
Ramanujan filter banks (RFB) have in the past been used to
identify periodicities in data. These are analysis filter banks
with no synthesis counterpart for perfect reconstruction of the
original signal, so they have not been useful for denoising periodic
signals. This paper proposes to use a hybrid analysissynthesis
framework for denoising discrete-time periodic signals.
The synthesis occurs via a pruned dictionary designed
based on the output energies of the RFB analysis filters. A
unique property of the framework is that the denoised output
signal is guaranteed to be periodic unlike any of the other
methods. For a large range of input noise levels, the proposed
approach achieves a stable and high SNR gain outperforming
many traditional denoising techniques.
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- Award ID(s):
- 1712633
- NSF-PAR ID:
- 10275658
- Date Published:
- Journal Name:
- Proc. IEEE Int. Conf. Acoust. Speech, and Signal Proc
- Page Range / eLocation ID:
- 5100 to 5104
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICASSP39728.2021.9413689
