Time and frequency parameters of sEMG signal — Force relationship

Achmamad, Abdelouahad; Belkhou, Abdelali; Jbari, Atman; Bellarbi, Larbi

doi:10.1109/icoa.2018.8370547

Cited by 18 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, equation (2) can be expressed in polar coordinates, i.e., aj(n) = Aj(n)e iφ j (n) where Aj(n) is the instantaneous amplitude of the signal, and φj(n) is the phase from which can be derived the instantaneous frequency fj(n) = fs 2π φj(n + 1) − φj(n) . Finally, the spectral features are derived considering the mean normalized frequencies (MNF) calculated as in [27] and the amplitude calculated as a mean of all instantaneous amplitude, obtaining a spectrogram spanning over 10 min. Fig.…”

Section: Feature Extractionmentioning

confidence: 99%

Audio-based Identification of Beehive States

Nolasco

Terenzi

Cecchi

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Audio-based Identification of Beehive States

Nolasco

Terenzi

Cecchi

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

View full text Add to dashboard Cite

show abstract

“…Root mean square, mean absolute value, zero-crossing rate, slope sign change, and waveform length of the MMG signals are selected as the time-domain features. 29 Median frequency, 30 mean power frequency, 30 and mean frequency 29 of the MMG signals are selected as the frequency-domain features. Sample entropy and Spearman's correlation coefficients of the MMG signals are selected as other features.…”

Section: Feature Extraction and Dimensions Reductionmentioning

confidence: 99%

A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals

Xie

Wang

et al. 2020

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

With the growth of the number of elderly and disabled with motor dysfunction, the demand for assisted exercise is increasing. Wearable power assistance robots are developed to provide athletic ability of limbs for the elderly or the disabled who have weakened limbs to better self-care ability. Existing wearable power-assisted robots generally use surface electromyography (sEMG) to obtain effective human motion intentions. Due to the characteristics of sEMG signals, it is limited in many applications. To solve the above problems, we design a long short-term memory (LSTM) neural network model based on human mechanomyography (MMG) signals to estimate the motion acceleration of knee joint. The acceleration can be further calculated by the torque required for movement control of the wearable power assistance robots for the lower limb. We detect MMG signals on the clothed thigh, extract features of the MMG signals, and then, use principal component analysis to reduce the features’ dimensions. Finally, the dimension-reduced features are inputted into the LSTM neural network model in time series for estimating the acceleration. The experimental results show that the average correlation coefficient ( R) is 94.48 ± 1.91% for the estimation of acceleration in the process of continuously performing under approximately π/4 rad/s. This approach can be applied in the practical applications of wearable field.

show abstract

“…A comparative study of wavelet families for electromyography signal… (Abdelouahad Achmamad) 1421 standard tool in the diagnostic of muscle disease and is commonly used for skeletal muscle investigations [8]. In fact, EMG has been traditionally utilized to assess the electrical potentials produced during contraction and relaxation of skeletal muscle [6], this technique found a wide application in multiple domains including rehabilitation, medicine prosthesis control, and clinical diagnosis [9]. In order to detect abnormalities for neuromuscular, EMG signal carries pertinent information regarding the state of the muscle.…”

Section: Introductionmentioning

confidence: 99%

“…In order to detect abnormalities for neuromuscular, EMG signal carries pertinent information regarding the state of the muscle. In general, the amplitude of the EMG signal varies approximately from 10μV to 5mV and the frequency content is concentrated in the range of 5Hz to 500Hz [9][10][11]. The EMG signal has been assumed as a non -stationary time series in which the statistical features and frequency content of the signal alter over time [12].…”

Section: Introductionmentioning

confidence: 99%

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

Achmamad

Jbari

2020

Bulletin EEI

Self Cite

View full text Add to dashboard Cite

Automatic detection of neuromuscular disorders performed using electromyography (EMG) has become an interesting domain for many researchers. In this paper, we present an approach to evaluate and classify the non-stationary EMG signals based on discrete wavelet transform (DWT). Most often researches did not consider the effect of DWT factors on the performance of EMG signals classification. This problem is still an interesting unsolved challenge. However, the selection of appropriate mother wavelet and related level decomposition is an essential issue that should be addressed in DWT-based EMG signals classification. The proposed method consists of decomposing a raw EMG signal into different sub-bands. Several statistical features were extracted from each sub-band and six wavelet families were investigated. The feature vector was used as inputs to support vector machine (SVM) classifier for the diagnosis of neuromuscular disorders. The obtained results achieve satisfactory performances with optimal DWT factors using 10-fold cross-validation. From the classification performances, it was found that sym14 is the most suitable mother wavelet at the 8th optimal wavelet level of decomposition. These simulation results demonstrated that the proposed method is very reliable for reducing cost computational time of automated neuromuscular disorders system and removing the redundancy information.

show abstract

Time and frequency parameters of sEMG signal — Force relationship

Cited by 18 publications

References 8 publications

Audio-based Identification of Beehive States

Audio-based Identification of Beehive States

A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals

A comparative study of wavelet families for electromyography signal classification based on discrete wavelet transform

Contact Info

Product

Resources

About