Understanding the effect of window length and overlap for assessing sEMG in dynamic fatiguing contractions: A non-linear dimensionality reduction and clustering

Fuente, Carlos De la; Martinez‐Valdes, Eduardo; Priego-Quesada, José Ignacio; Weinstein, Alejandro; Valencia, O.; Kunzler, Marcos Roberto; Alvarez-Ruf, Joel; Carpes, Felipe Pivetta

doi:10.1016/j.jbiomech.2021.110598

Cited by 11 publications

(6 citation statements)

References 46 publications

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“…It has been suggested that WT and STFT should be suitable for analyzing the nonstationary myoelectric signals that are recorded during dynamic contractions [38,39]. Our study confirms that because of the Spearman's rank correlation coefficient between WT_RMS and STFT_RMS and RPE is higher than RMS (Table 2).…”

Section: Discussionsupporting

confidence: 82%

A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method

Zhang

et al. 2022

Electronics

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The rating of perceived exertion (RPE) and surface electromyography (sEMG) describe exercise intensity subjectively and objectively, while there has been a lack of research on the relationship between them during dynamic contractions to predict exercise intensity, comprehensively. The purpose of this study was to establish a model of the relationship between sEMG and RPE during dynamic exercises. Therefore, 20 healthy male subjects were organized to perform an incremental load test on a cycle ergometer, and the subjects’ RPEs (Borg Scale 6–20) were collected every minute. Additionally, the sEMGs of the subjects’ eight lower limb muscles were collected. The sEMG features based on time domain, frequency domain and time–frequency domain methods were extracted, and the relationship model was established using Gaussian process regression (GPR). The results show that the sEMG and RPE of the selected lower limb muscles are significantly correlated (p < 0.05) but that they have different monotonic correlation degrees. The model that was established with all three domain features displayed optimal performance and when the RPE was 13, the prediction error was the smallest. The study is significant for lower limb muscle training strategy and quantification of training intensity from both subjective and objective aspects, and lays a foundation for sEMG further applications in rehabilitation medicine and sports training.

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Section: Discussionsupporting

confidence: 82%

A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method

Zhang

et al. 2022

Electronics

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“…The sample included 73,008 sEMG activation maps obtained from seven healthy participants (aged 21.4 ± 1.5 years, body mass 74.5 ± 8.5 kg, height 1.77 ± 0.01 m, and body mass index 20.9 ± 2.2 kg/m 2 ) who performed a submaximal isometric plantar flexion with the ankle at neutral position (60% with the ankle in a 90° position) in a controlled laboratory set-up ( Figure 1 ). Here, we considered the medial gastrocnemius muscle as a good muscle model due to its application in clinics and biomechanics and because it was previously used in EMG segmentation ( Theisen et al, 2016 ; De la Fuente et al, 2021 ). The Bioethics Committee of the Andes University (Santiago, Chile) approved this study (# INV-IN201701), which was developed according to the principles of the Declaration of Helsinki.…”

Section: Methodsmentioning

confidence: 99%

“…Previously, sEMG segmentation influenced the electrical manifestation of fatigue conclusions ( De la Fuente et al, 2021 ). Since sEMG activation maps depend inherently on segmentation, alterations are expected in the sEMG activation map.…”

Section: Introductionmentioning

confidence: 99%

Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis

Fuente

Weinstein

Neira

et al. 2022

Front. Bioeng. Biotechnol.

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The instantaneous spatial representation of electrical propagation produced by muscle contraction may introduce bias in surface electromyographical (sEMG) activation maps. Here, we described the effect of instantaneous spatial representation (sEMG segmentation) on embedded fuzzy topological polyhedrons and image features extracted from sEMG activation maps. We analyzed 73,008 topographic sEMG activation maps from seven healthy participants (age 21.4 ± 1.5 years and body mass 74.5 ± 8.5 kg) who performed submaximal isometric plantar flexions with 64 surface electrodes placed over the medial gastrocnemius muscle. Window lengths of 50, 100, 150, 250, 500, and 1,000 ms and overlap of 0, 25, 50, 75, and 90% to change sEMG map generation were tested in a factorial design (grid search). The Shannon entropy and volume of global embedded tri-dimensional geometries (polyhedron projections), and the Shannon entropy, location of the center (LoC), and image moments of maps were analyzed. The polyhedron volume increased when the overlap was <25% and >75%. Entropy decreased when the overlap was <25% and >75% and when the window length was <100 ms and >500 ms. The LoC in the x-axis, entropy, and the histogram moments of maps showed effects for overlap (p < 0.001), while the LoC in the y-axis and entropy showed effects for both overlap and window length (p < 0.001). In conclusion, the instantaneous sEMG maps are first affected by outer parameters of the overlap, followed by the length of the window. Thus, choosing the window length and overlap parameters can introduce bias in sEMG activation maps, resulting in distorted regional muscle activation.

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“…This window keeps sliding over the upcoming samples and provides time-localized frequency information. An increase in the window size would increase the frequency resolution and reduce the time resolution and vice versa [89]. The window size is chosen by convention for most applications and once chosen, is not considered further.…”

Section: Input Representations For U-netmentioning

confidence: 99%

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

Gul,

Khan

2023

IEEE Access

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The recent surge in the use of Deep Neural Networks (DNNs) has also made its mark in the field of Audio Enhancement (AE), providing much better quality than the classical methods. Although, there are dedicated audio processing DNNs, yet, many recent models of AE have utilized U-Net: a DNN based on Convolutional Neural Network (CNN), fundamentally developed for image segmentation. It is found that the useful features hidden in the time domain are highlighted when the audio signal is converted to a spectrogram, which can be treated as an image. In this article, we will review the recent work, utilizing U-Nets for different AE applications. Different than other published reviews, this review focuses entirely on AE techniques based on image U-Nets. We will discuss the need for AE, U-Net comparison to other DNNs, the benefits of converting the audio to 2D, input representations that are useful for different AE applications, the architecture of vanilla U-Net and the pre-trained models, variations in vanilla architecture incorporated in different E models, and the state-of-the-art AE algorithms based on U-Net in various applications. Apart from speech and music, this article discusses a wide range of audio signals e.g. environmental, biomedical, bioacoustics, and industrial sounds, not covered collectively in a single article in previously published studies. The article ends with the discussion of colored spectrograms in future AE applications.INDEX TERMS CNNs; image processing deep neural networks; pre-trained networks; spectrogram.;U-Net.

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Understanding the effect of window length and overlap for assessing sEMG in dynamic fatiguing contractions: A non-linear dimensionality reduction and clustering

Cited by 11 publications

References 46 publications

A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method

A Study on the Relationship between RPE and sEMG in Dynamic Contraction Based on the GPR Method

Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis

A Survey of Audio Enhancement Algorithms for Music, Speech, Bioacoustics, Biomedical, Industrial, and Environmental Sounds by Image U-Net

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