An automatic SSA-based de-noising and smoothing technique for surface electromyography signals

Romero, Francisco; Sánchez, Francisco Javier Alonso; Cubero, Javier Segovia; Galán-Marín, Gloria

doi:10.1016/j.bspc.2015.02.005

Cited by 38 publications

(27 citation statements)

References 12 publications

(23 reference statements)

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“…The window length must be chosen in order to produce an adequate separability of the temporal series reconstructed from each elementary matrix. Regarding the components to reconstruct the signal, new algorithms based on SSA are needed to automate the filtering process [33]. Moreover, further experimental tests are required to extend the applicability of the method to different loads and different muscle groups.…”

Section: Resultsmentioning

confidence: 99%

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

Romero

Sánchez

Gragera

et al. 2016

J Braz. Soc. Mech. Sci. Eng.

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The estimation of muscular forces is useful in several areas such as Biomedical or Rehabilitation Engineering. As muscular forces cannot be measured in vivo non-invasively they must be estimated by using indirect measurements such as surface electromyography (sEMG) signals or by means of Inverse Dynamic (ID) analyses. This paper proposes an approach to estimate muscular forces based on both of them. The main idea is to tune a gain matrix so as to compute muscular forces from sEMG signals. To do so, a curve fitting process based on least-squares is carried out. The input is the sEMG signal filtered using Singular Spectrum Analysis technique. The output corresponds to the muscular force estimated by the ID analysis of the recorded task, a dumbbell weightlifting. Once the model parameters are tuned, it is possible to obtain an estimation of muscular forces based on sEMG signal. This procedure might be used to predict muscular forces in vivo outside the space limitations of the gait analysis laboratory.

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Section: Resultsmentioning

confidence: 99%

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

Romero

Sánchez

Gragera

et al. 2016

J Braz. Soc. Mech. Sci. Eng.

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“…Then, a thin layer of conductive gel was extended along the point of application of the electrode, that is, in the middle zone of the muscle, far from innervated an tendinosus zones (Criswell, 2010). The sEMG signals were filtered by means of singular spectrum analysis (see Romero et al, 2015 for further details) and the first component was retrieved as the trend of the signal, i.e., the input to the A-model. The values were normalized to the maximum voluntary contraction (MVC) following the recommendation given in Konrad (2006).…”

Section: Methodsmentioning

confidence: 99%

A comparison among different Hill-type contraction dynamics formulations for muscle force estimation

Romero

Sánchez

2016

Mech. Sci.

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Abstract.Muscle is a type of tissue able to contract and, thus, shorten, producing a pulling force able to generate movement. The analysis of its activity is essential to understand how the force is generated to perform a movement and how that force can be estimated from direct or indirect measurements. Hill-type muscle model is one of the most used models to describe the mechanism of force production. It is composed by different elements that describe the behaviour of the muscle (contractile, series elastic and parallel elastic element) and tendon. In this work we analyze the differences between different formulations found in the literature for these elements. To evaluate the differences, a flexo-extension movement of the arm was performed, using as input to the different models the surface electromyography signal recorded and the muscle-tendon lengths and contraction velocities obtained by means of inverse dynamic analysis. The results show that the force predicted by the different models is similar and the main differences in muscle force prediction were observed at full-flexion. The results are expected to contribute in the selection of the different formulations of Hill-type muscle model to solve a specific problem.

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“…Additionally, 11 surface EMG signals on the right leg were recorded 81 at 1 kHz (BTS, FREEEMG). Each EMG signal was rectified, filtered by singular spectrum analysis 82 (SSA) with a window length of 250 (Romero et al 2015) (equivalent to the common forward and 83 reverse low-pass 5th order Butterworth filter with a cut-off frequency of 15 Hz) and then normalized 84 with respect to its maximal value as recommended in (Raison et al 2011). 85…”

Section: Experimental Data Collection 75mentioning

confidence: 99%

Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?

Michaud

Shourijeh

Fregly

et al. 2019

Preprint

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10Determination of muscle forces during motion can help to understand motor control, assess 11 pathological movement , diagnose neuromuscular disorders, or estimate joint loads. Difficulty of in 12 vivo measurement made computational analysis become a common alternative in which, as several 13 muscles serve each degree of freedom, the muscle redundancy problem must be solved. Unlike static 14 optimization (SO), synergy optimization (SynO) couples muscle activations across all time frames, 15 thereby altering estimated muscle co-contraction. This study explores whether the use of a muscle 16 synergy structure within a static optimization framework improves prediction of muscle activations 17 during walking. A motion/force/EMG gait analysis was performed on five healthy subjects. A 18 musculoskeletal model of the right leg actuated by 43 Hill-type muscles was scaled to each subject 19 and used to calculate joint moments, muscle-tendon kinematics and moment arms. Muscle activations 20 were then estimated using SynO with two to six synergies and traditional SO, and these estimates 21were compared with EMG measurements. SynO neither improved SO prediction of experimental 22 activation patterns nor provided SO exact matching of joint moments. Finally, synergy analysis was 23 performed on SO estimated activations, being found that the reconstructed activations produced poor 24 matching of experimental activations and joint moments. As conclusion, it can be said that, although 25SynO did not improve prediction of muscle activations during gait, its reduced dimensional control 26 space could be beneficial for applications such as functional electrical stimulation (FES) or motion 27 control and prediction. 28

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An automatic SSA-based de-noising and smoothing technique for surface electromyography signals

Cited by 38 publications

References 12 publications

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

Estimation of muscular forces from SSA smoothed sEMG signals calibrated by inverse dynamics-based physiological static optimization

A comparison among different Hill-type contraction dynamics formulations for muscle force estimation

Do Muscle Synergies Improve Optimization Prediction of Muscle Activations During Gait?

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