Does the fast Fourier transformation window length affect the slope of an electromyogram's median frequency plot during a fatiguing isometric contraction?

Hollman, John H.; Hohl, Jeffrey M.; Kraft, Jordan L.; Strauss, Jeffrey D.; Traver, Katie J.

doi:10.1016/j.gaitpost.2012.10.028

Cited by 10 publications

(9 citation statements)

References 10 publications

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“…Second, the effect of muscle fatigue on the frequency response of muscles fibers [65] can be emulated, by altering the calculated spectrogram. The idea is to reduce the median frequency of a channel with a certain probability, by systematically redistributing part of the power of a frequency bin to an adjacent lower frequency one and so on.…”

Section: Appendix a Data Augmentationmentioning

confidence: 99%

“…The idea is to reduce the median frequency of a channel with a certain probability, by systematically redistributing part of the power of a frequency bin to an adjacent lower frequency one and so on. This was done in order to approximate the effect of muscle fatigue on the frequency response of muscle fibers [65]. In this work, this technique will be referred to as Muscle Fatigue augmentation.…”

Section: Appendix a Data Augmentationmentioning

confidence: 99%

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Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Côté-Allard¹,

Fall²,

Drouin³

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

574

378

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In recent years, deep learning algorithms have become increasingly more prominent for their unparalleled ability to automatically learn discriminant features from large amounts of data. However, within the field of electromyographybased gesture recognition, deep learning algorithms are seldom employed as they require an unreasonable amount of effort from a single person, to generate tens of thousands of examples.This work's hypothesis is that general, informative features can be learned from the large amounts of data generated by aggregating the signals of multiple users, thus reducing the recording burden while enhancing gesture recognition. Consequently, this paper proposes applying transfer learning on aggregated data from multiple users, while leveraging the capacity of deep learning algorithms to learn discriminant features from large datasets. Two datasets comprised of 19 and 17 able-bodied participants respectively (the first one is employed for pre-training) were recorded for this work, using the Myo Armband. A third Myo Armband dataset was taken from the NinaPro database and is comprised of 10 able-bodied participants. Three different deep learning networks employing three different modalities as input (raw EMG, Spectrograms and Continuous Wavelet Transform (CWT)) are tested on the second and third dataset. The proposed transfer learning scheme is shown to systematically and significantly enhance the performance for all three networks on the two datasets, achieving an offline accuracy of 98.31% for 7 gestures over 17 participants for the CWT-based ConvNet and 68.98% for 18 gestures over 10 participants for the raw EMG-based ConvNet. Finally, a use-case study employing eight able-bodied participants suggests that real-time feedback allows users to adapt their muscle activation strategy which reduces the degradation in accuracy normally experienced over time.

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Section: Appendix a Data Augmentationmentioning

confidence: 99%

Section: Appendix a Data Augmentationmentioning

confidence: 99%

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Côté-Allard¹,

Fall²,

Drouin³

et al. 2019

IEEE Trans. Neural Syst. Rehabil. Eng.

574

378

View full text Add to dashboard Cite

show abstract

“…The MDF was defined as the frequency that divided the spectrum into two equal areas. Each signals was then divided in 0.5s windows without overlap for which an MDF value was computed [ 34 ]. More specifically, MDF values were obtained through each electrode signal.…”

Section: Methodsmentioning

confidence: 99%

Muscle Activity Adaptations to Spinal Tissue Creep in the Presence of Muscle Fatigue

2016

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AimThe aim of this study was to identify adaptations in muscle activity distribution to spinal tissue creep in presence of muscle fatigue.MethodsTwenty-three healthy participants performed a fatigue task before and after 30 minutes of passive spinal tissue deformation in flexion. Right and left erector spinae activity was recorded using large-arrays surface electromyography (EMG). To characterize muscle activity distribution, dispersion was used. During the fatigue task, EMG amplitude root mean square (RMS), median frequency and dispersion in x- and y-axis were compared before and after spinal creep.ResultsImportant fatigue-related changes in EMG median frequency were observed during muscle fatigue. Median frequency values showed a significant main creep effect, with lower median frequency values on the left side under the creep condition (p≤0.0001). A significant main creep effect on RMS values was also observed as RMS values were higher after creep deformation on the right side (p = 0.014); a similar tendency, although not significant, was observed on the left side (p = 0.06). A significant creep effects for x-axis dispersion values was observed, with higher dispersion values following the deformation protocol on the left side (p≤0.001). Regarding y-axis dispersion values, a significant creep x fatigue interaction effect was observed on the left side (p = 0.016); a similar tendency, although not significant, was observed on the right side (p = 0.08).ConclusionCombined muscle fatigue and creep deformation of spinal tissues led to changes in muscle activity amplitude, frequency domain and distribution.

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“…MDF is the frequency that divides the total sum of the sEMG power spectrum into two segments that are equal in amplitude (Oskoei et al [10]). It has been found that a decreasing MDF over time indicates fatigue (Hollman et al [11]). Besides these frequency-based features, the other spectral variables which have been employed in sEMG signal analysis include the power spectrum, root mean square values, peak frequency, and the spectral indexes (Farina and Merletti [12] and Phinyomark et al [6]).…”

Section: Complexitymentioning

confidence: 99%

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

Wang

et al. 2018

Complexity

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To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists.

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Does the fast Fourier transformation window length affect the slope of an electromyogram's median frequency plot during a fatiguing isometric contraction?

Cited by 10 publications

References 10 publications

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning

Muscle Activity Adaptations to Spinal Tissue Creep in the Presence of Muscle Fatigue

Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals

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