Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments

Messaoudi, Noureddine; Bekka, Raïs El’hadi; Ravier, Philippe; Harba, Rachid

doi:10.1016/j.jelekin.2016.12.006

Cited by 12 publications

(26 citation statements)

References 36 publications

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“…Even though the peak of the EMG stationarity level was found at the 512-samples window depending on one subject, on average, there existed a small decrement and increment between 512 ms and 1024 ms window size. Meanwhile, the stationarity of EMG was at the highest with 250 ms window size and lowest at 1000 ms window size in the MRA test compared to 500 and 2000 ms [25]. Nonetheless, most of the available studies concentrated on isometric contraction.…”

Section: Discussionmentioning

confidence: 98%

“…As adjacent windowing techniques are widely adopted in the literature to analyse the stationarity of EMG signals [21,[24][25][26], only adjacent windowing techniques were considered in this study. Adjacent windowing is where disjointed segments with a predefined length, S i use feature extraction and classification after a certain processing delay, τ as shown in Figure 4.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

“…The τ is the processing time required to calculate the feature and classify the data. Adjacent windowing approach is commonly utilised to determine whether the EMG signals remain stationary or otherwise [21,[24][25][26]. In this study, the data were divided into adjacent segments with the length of window size 100, 200, 500, and 1000 (ms).…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

“…The stationarity analysis was applied to the last 4 s, which was the first segment since the level of the excitatory drove over the initial 1 s was not sufficient to recruit all motor unit muscles [25]. The first segment was segmented into three window sizes including 100, 200, 500 and 1000 ms to test the local stationarity of each segment.…”

Section: Stationary Testsmentioning

confidence: 99%

“…The study revealed that the stationarity of EMG signals decreases as trunk extension angular velocity increases as much as 30 degrees with 1000 ms window size based on RA and MRA tests. In the case of isometric contraction which occurs during static positions (the joint angle and muscle length do not change), the stationarity level of EMG signals depends on the window size [25]. With 375 ms and 125 ms window size, EMG signals are reported as stationary during isometric and isotonic contractions, respectively [26].…”

Section: Introductionmentioning

confidence: 99%

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Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

et al. 2017

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Abstract:In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying window sizes. Therefore, in this study, the authors endeavoured to determine the suitable window size to analyse EMG signals during isotonic contractions utilising stationary tests, reverse arrangement (RA), and modified reverse arrangement (MRA). There were slight differences in the average percentages of signal stationarity for RA and MRA tests in 100 ms, 500 ms, and 1000 ms window sizes. However, there was none for the 200 ms window size. On average, a window size of 200 ms provided stationary information with 88.57% of EMG signals compared to other window sizes. This study also recommended the MRA test to determine EMG signals stationarity for future studies, as the performances were better in comparison to RA tests. However, the following recommendation is only valid for window sizes greater than 200 ms. For a real-time application, the size of the analysis window together with the processing time should be less than 300 ms and a window size of 200 ms is applicable for isotonic contractions.

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Section: Discussionmentioning

confidence: 98%

Section: Data Acquisition and Processingmentioning

confidence: 99%

Section: Data Acquisition and Processingmentioning

confidence: 99%

Section: Stationary Testsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

et al. 2017

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EMG pattern recognition via Bayesian inference with scale mixture-based stochastic generative models

Furui

Igaue

Tsuji

2021

Expert Systems with Applications

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A Laguerre–Volterra network model based on ant colony optimization applied to evaluate EMG-force relationship in the muscle fatigue state

Ma,

Luo,

Xiahou

et al. 2024

Review of Scientific Instruments

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With the accuracy and convenience improvement of electromyographic (EMG) acquired by wearable devices, EMG is gradually used to evaluate muscle force signal, a non-invasive evaluation method. However, the relationship between EMG and force is a complex nonlinear relationship, even which will change with different movements and different muscle states. Therefore, it is difficult to evaluate this nonlinear EMG–force relationship, especially when the muscle state gradually transits from non-fatigue to deep fatigue. For more accurate values of force in human fatigue state, this paper proposes a dual-input Laguerre–Volterra network (LVN) model based on ant colony optimization. First, the changes in 19 EMG features are discussed with increasing fatigue. We also consider two non-Gaussian features: kurtosis and negentropy in the 19 features. Later, 11 EMG fatigue features are picked out according to the fatigue test. Then, the preprocessed EMG and a composite signal of the 11 fatigue features are simultaneously input into the LVN model. Subsequently, the ant colony optimization algorithm is selected to train the model parameters. At the same time, a penalty term that we defined is introduced into the model cost function to adjust the weight of each feature adaptively. Finally, some experiments prove that the LVN model could quick fit the accurate force signal in five fatigue stages, such as non-fatigue, slight fatigue, mild fatigue, severe fatigue, and extreme fatigue. This LVN model can quickly transform EMG into strength signal in real time, which is suitable for people to observe muscle strength by a wearable device and makes it easy to detect the muscle current state. This model has good stability and can remain effective for a long time with training once, which provides convenience for the users of wearable devices.

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Assessment of the non-Gaussianity and non-linearity levels of simulated sEMG signals on stationary segments

Cited by 12 publications

References 36 publications

Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

Assessment on Stationarity of EMG Signals with Different Windows Size During Isotonic Contractions

EMG pattern recognition via Bayesian inference with scale mixture-based stochastic generative models

A Laguerre–Volterra network model based on ant colony optimization applied to evaluate EMG-force relationship in the muscle fatigue state

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