Research on Improved Wavelet Denoising Method for sEMG Signal

Liu, Cong; Cao, Dianguo; Yuan, Yaoyao

doi:10.1109/cac48633.2019.8996945

Cited by 13 publications

(6 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To choose the best MNF components for reconstruction, we use the composite evaluation index (CEI) method. This method was used by Booysen, 2002, Zhu et al ., 2015, Li et al ., 2017, Li et al ., 2019, He et al ., 2019 and Ming et al ., 2019 to determine the decomposition layer number of wavelet denoising. The normalized root mean square error (RMSE) and smoothness (

r

) of the denoised data are adopted to generate a CEI based on the coefficient of variation (Lovie, 2005; Dellwo et al ., 2006).…”

Section: Synthetic and Field Examplesmentioning

confidence: 99%

Suppression of motion noise based on a linear‐homomorphic filtering algorithm in airborne electromagnetic survey

Peng

Jing

Fan

et al. 2021

Geophysical Prospecting

View full text Add to dashboard Cite

The movement of the receiver coil in a magnetic field produces motion noise in the airborne electromagnetic field. With an amplitude that is several orders of magnitude higher than that of other noise sources, this motion noise is one of the main low‐frequency (<1 kHz) noise sources. Especially in the late decay period, the residual motion noise produces false anomalous information, which is a key problem of low‐base‐frequency airborne electromagnetic surveys. In this paper, a method for calculating the motion noise is proposed. This method takes into account both the earth's magnetic field and the active secondary magnetic field and introduces additive and multiplicative motion noise. Then, a linear‐homomorphic filtering algorithm is proposed to handle the additive and multiplicative motion noises. In the linear filtering part of our algorithm, the minimum noise fraction transform is applied to suppress additive motion noise. It provides sets of components ordered by the signal‐to‐noise ratio by deriving the eigenvalues and eigenvectors for the signal and uncorrelated noise covariance matrices. In the homomorphic filtering part, the multiplicative motion noise is removed. To do this, the adaptive‐width smoothing algorithm is applied to the logarithm of the filtered with the linear filtering process profile data. The results of synthetic and field data demonstrate that the linear filtering process based on the minimum noise fraction transform is effective for very low signal‐to‐noise ratios of the late‐time data, and the homomorphic filtering process can help distinguish small anomalies of these channels.

show abstract

r

) of the denoised data are adopted to generate a CEI based on the coefficient of variation (Lovie, 2005; Dellwo et al ., 2006).…”

Section: Synthetic and Field Examplesmentioning

confidence: 99%

Suppression of motion noise based on a linear‐homomorphic filtering algorithm in airborne electromagnetic survey

Peng

Jing

Fan

et al. 2021

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

“…The sEMG signal is a nonstationary, nonlinear, weak electrical signal that is very susceptible to interference from environmental noise and power frequency noise. Before feature extraction and subsequent processing, it is necessary to denoise the sEMG signal and use the improved wavelet denoising method [36] to filter out the signal noise, power frequency interference and baseline drift and obtain a smooth sEMG signal with less noise. The literature [36] has already introduced improved wavelet denoising in detail, so this paper will not go into details.…”

Section: Analysis Of Suraface Electromyography Physiological Characteristics In Action Recognitionmentioning

confidence: 99%

“…Before feature extraction and subsequent processing, it is necessary to denoise the sEMG signal and use the improved wavelet denoising method [36] to filter out the signal noise, power frequency interference and baseline drift and obtain a smooth sEMG signal with less noise. The literature [36] has already introduced improved wavelet denoising in detail, so this paper will not go into details. Here, we consider subject No.…”

Section: Analysis Of Suraface Electromyography Physiological Characteristics In Action Recognitionmentioning

confidence: 99%

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

Wang

Cao

Liu

2021

Sensors

Self Cite

View full text Add to dashboard Cite

To improve the recognition rate of lower limb actions based on surface electromyography (sEMG), an effective weighted feature method is proposed, and an improved genetic algorithm support vector machine (IGA-SVM) is designed in this paper. First, for the problem of high feature redundancy and low discrimination in the surface electromyography feature extraction process, the weighted feature method is proposed based on the correlation between muscles and actions. Second, to solve the problem of the genetic algorithm selection operator easily falling into a local optimum solution, the improved genetic algorithm-support vector machine is designed by championship with sorting method. Finally, the proposed method is used to recognize six types of lower limb actions designed, and the average recognition rate reaches 94.75%. Experimental results indicate that the proposed method has definite potentiality in lower limb action recognition.

show abstract

“…The agreement ratio of the shared secret sequence generated by the channel estimation is mainly affected by noise. In recent years, wavelet threshold denoising is often used in signal processing fields such as image denoising, and it has good results in various fields [31], [32]. In this paper, wavelet threshold denoising is used for channel estimation, which can reduce the interference of noise to the system and improve the performance of the system.…”

Section: A Robustnessmentioning

confidence: 99%

A Tag-Based PHY-Layer Authentication Scheme Without Key Distribution

Zhang

2021

IEEE Access

View full text Add to dashboard Cite

Authentication is the process of confirming the legal identity of communicating entities, and it is the first line of defense for security communication. Most of the existing tag-based physical layer security authentication (PLSA) requires distributing the shared keys in advance. In the large scale internet of things scenario, nodes frequently join and leave the wireless networks that cause the distribution and management of keys particularly difficult. This paper proposes a tag-based PLSA scheme, which utilizes channel characteristics instead of distributing keys to generate authentication tags. Specifically, based on watermarking mechanism, we design a fault-tolerant hash algorithm to couple the secret sequence and the message signal for authentication tags generation. The shared secret sequence is generated by legitimate nodes through channel probing. And the theories of information theory and composite hypothesis testing is employed to analyze the performance of system. The simulation results show that the agreement ratio of the generating shared secret sequence is as high as 96% in the case of high signal-to-noise ratio and low power tag embedding. In addition, performance analysis demonstrates the scheme can resist against multiple attacks, such as replay, jamming, tampering, and impersonation attack.INDEX TERMS Authentication, wireless network, tag-based, hash algorithm, channel probing.

show abstract

Research on Improved Wavelet Denoising Method for sEMG Signal

Cited by 13 publications

References 1 publication

Suppression of motion noise based on a linear‐homomorphic filtering algorithm in airborne electromagnetic survey

Suppression of motion noise based on a linear‐homomorphic filtering algorithm in airborne electromagnetic survey

Action Recognition of Lower Limbs Based on Surface Electromyography Weighted Feature Method

A Tag-Based PHY-Layer Authentication Scheme Without Key Distribution

Contact Info

Product

Resources

About