An EMG comparative analysis of quadriceps during isoinertial strength training using nonlinear scaled wavelets

Napoli, Nicholas J.; Mixco, Anthony; Bohórquez, Jorge; Signorile, Joseph F.

doi:10.1016/j.humov.2014.12.009

Cited by 18 publications

(12 citation statements)

References 37 publications

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“…On the one hand, the use of wavelet analysis has become more common [52] in the denoising of the sEMG signal. Wavelet analysis is not affected by the non-stationary properties of sEMG signals [53].…”

Section: Methodsmentioning

confidence: 99%

“…In sEMG signal analysis, the use of wavelets has become more usual [53] because it applies certain tools that change the time resolution for each frequency. In this way, it gives a higher time result for high bands and a lower resolution time for low-frequency bands.…”

Section: Methodsmentioning

confidence: 99%

“…The use of an orthogonal basis implies the use of the discrete wavelet transform. Several studies [52,53,58,60], in which the analysed signal was similar to this study, were consulted in order to establish a first approximation of which wavelet functions could fit better in this work. On the basis of that, different wavelet functions (in particular, Daubechies and Symlets wavelets) with different number of decomposition levels were checked in this study.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?

Toro

Santos-Cuadros

Olmeda

et al. 2019

Sensors

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Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?

Toro

Santos-Cuadros

Olmeda

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…(1)The AR model order: The Levinson-Durbin recursive algorithm (Farmer et al, 2014;Al-Angari et al, 2016) and the empirical value can be used to solve the AR model coefficients. Previous research showed that when the order was 3~5 (Gokgoz and Subasi, 2014;Napoli et al, 2015), the AR model can represent the feature of the sEMG signal perfectly. Here 4 th order was chosen for the AR model.…”

Section: Design Of the Pattern Recognition Algorithm 51 Feature Extrmentioning

confidence: 99%

The design of a hemiplegic upper limb rehabilitation training system based on surface EMG signals

Zhang

Dai

et al. 2018

JAMDSM

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In this paper, we designed a hemiplegic upper limb rehabilitation training system, which allowed both single degree of freedom and composite degrees of freedom for the training of shoulder and elbow. The system contained an upper limb rehabilitation robot, a pattern recognition system and a motion control system. Firstly, we designed a novel upper limb rehabilitation robot with three degrees of freedom, with the motor and reducer innovatively placed centrally in the shoulder of the mechanical limb arm. The movement is more stable because the inertia of each joint movement is reduced. The design makes simultaneously training both the left and right arm possible. In the control system design, the movement coupling problem is solved through the inverse operation of the target action. Lastly, to further enrich information of the single feature vector, a method integrating the AR model coefficients and wavelet coefficients was proposed. A method combining the Particle Swarm Optimization Algorithm and Levenberg-Marquardt (LM) was used to optimize the BP networks, addressing the problems associated with lower convergence speed and local minimum of standard BP networks. The experiments showed that the convergence speed of the network and the recognition rate of the target action were effectively improved, which demonstrated the effectiveness of the training system.

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“…(Cormie et al, 2011) Tests of muscle power involve dynamic movements that can be single or multi-joint, open or closed chain. (Neeter et al, 2006) Free weights and machines using weight stacks can be used to measure power or perform power training exercises (Kyrolainen et al, 2005), but pneumatic resistance devices may be most appropriate (Napoli et al, 2014) due to the ability to provide a more constant resistance throughout the contraction which allows for greater movement velocities. (Frost et al, 2008) Surrogate measurements of power have been developed that don't require expensive equipment or significant time for analysis and are appropriate for clinical use.…”

Section: Powermentioning

confidence: 99%

Lower extremity power and knee extensor rapid force development after knee injury, surgery, and rehabilitation

Cobian¹

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An EMG comparative analysis of quadriceps during isoinertial strength training using nonlinear scaled wavelets

Cited by 18 publications

References 37 publications

Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?

Is the Use of a Low-Cost sEMG Sensor Valid to Measure Muscle Fatigue?

The design of a hemiplegic upper limb rehabilitation training system based on surface EMG signals

Lower extremity power and knee extensor rapid force development after knee injury, surgery, and rehabilitation

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