Análise espectral do sinal EMG de exercício incremental em ciclistas e não ciclistas usando as transformadas de Fourier e Wavelet

Vitor-Costa, Marcelo; Bortolotti, Henrique; Camata, Thiago Viana; Cardoso, Jefferson Rosa; Silva, Rubens Alexandre da; Abrão, Taufik; Moraes, Antônio Carlos de; Altimari, Leandro Ricardo

doi:10.5007/1980-0037.2012v14n6p660

Cited by 4 publications

(2 citation statements)

References 19 publications

(22 reference statements)

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“…The fundamental confinement of a FFT is that it cannot give simultaneous time and frequency localization [83]. Therefore, investigation of the EMG signal in dynamic contraction (i.e., repetitive lifting) utilizing these methods may not be successful since it requires the signal to be stationary.…”

Section: Electromyography Processingmentioning

confidence: 99%

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Shair

Ahmad

Marhaban

et al. 2017

BioMed Research International

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Manual lifting is one of the common practices used in the industries to transport or move objects to a desired place. Nowadays, even though mechanized equipment is widely available, manual lifting is still considered as an essential way to perform material handling task. Improper lifting strategies may contribute to musculoskeletal disorders (MSDs), where overexertion contributes as the highest factor. To overcome this problem, electromyography (EMG) signal is used to monitor the workers' muscle condition and to find maximum lifting load, lifting height and number of repetitions that the workers are able to handle before experiencing fatigue to avoid overexertion. Past researchers have introduced several EMG processing techniques and different EMG features that represent fatigue indices in time, frequency, and time-frequency domain. The impact of EMG processing based measures in fatigue assessment during manual lifting are reviewed in this paper. It is believed that this paper will greatly benefit researchers who need a bird's eye view of the biosignal processing which are currently available, thus determining the best possible techniques for lifting applications.

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Section: Electromyography Processingmentioning

confidence: 99%

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Shair

Ahmad

Marhaban

et al. 2017

BioMed Research International

View full text Add to dashboard Cite

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“…It has also been extended to assess human musculoskeletal force (Abdelouahad et al, 2018). Other examples of employing FFT include sEMG-based analysis of wrist and gripping movements over various arm positions (Ryait et al, 2010), evaluation of the electromyographic indexes of fatigue during cycling activity in trained and untrained people using Wavelet Transform (WT) and FFT (Vitor-Costa et al, 2012), EMG data analysis used to investigate gait abnormalities (Singh et al, 2019), and sEMG based silent speech recognition on neuromuscular activities by using Short Time Fourier Transform (STFT) (Wang et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Gender Differences in Taste Sensations Based on Frequency Analysis of Surface Electromyography

et al. 2023

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Males and females respond differently at the muscular level to various tastes and show varied responses when eating different foods. In this study, we used surface electromyography (sEMG) as a novel approach to examine gender differences in taste sensations. We collected sEMG data from 30 participants (15 males, 15 females) over various sessions for six taste states: a no-stimulation physiological state, sweet, sour, salty, bitter, and umami. We applied a Fast Fourier Transformation to the sEMG-filtered data and used a two-sample t-test algorithm to analyze and evaluate the resulting frequency spectrum. Our results showed that the female participants had more sEMG channels with low frequencies and fewer channels with high frequencies than the male participants during all taste states except the bitter taste sensation, meaning that for most sensations, the female participants had better tactile and fewer gustatory responses than the male participants. The female participants responded better to gustatory and tactile perceptions during bitter tasting because they had more channels throughout the frequency distribution. Moreover, the facial muscles of the female participants twitched with low frequencies, while the facial muscles of the male participants twitched with high frequencies for all taste states except the bitter sensation, for which the female facial muscles twitched throughout the range of the frequency distribution. This gender-dependent variation in sEMG frequency distribution provides new evidence of differentiated taste sensations between males and females.

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Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Ramos

Vaz

Mendonça

et al. 2020

Journal of Healthcare Engineering

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Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82±0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.

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Análise espectral do sinal EMG de exercício incremental em ciclistas e não ciclistas usando as transformadas de Fourier e Wavelet

Cited by 4 publications

References 19 publications

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

EMG Processing Based Measures of Fatigue Assessment during Manual Lifting

Gender Differences in Taste Sensations Based on Frequency Analysis of Surface Electromyography

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

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