Evaluating the Electromyographical Signal During Symmetrical Load Lifting

Loss, Jefferson Fagundes; Cantergi, Debora; Krumholz, Fábia Milman; Torre, Marcelo La; Candotti, Cláudia Tarragô

doi:10.5772/25732

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…As the task of lifting objects from the ground exposes spinal structures to muscularskeletal overload it has been consistently investigated. In addition, epidemiological research associates lifting to the risk of developing lumbar back pain [1].…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Force in Human Muscle Using EMG in Hot Rolling Mill

Govindaraju¹

2015

AJSS

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The electromyography (EMG) is the measure of electrical activity produced by the muscles which is usually represented as a function of time. This electromyography can be used in various applications including identifying neuromuscular diseases, control signal for prosthetic devices, controlling machines, robots etc. The existing system commercial EMG-controlled devices are limited to rudimentary control capabilities of either discrete states (e.g. hand close/open), or one degree of freedom proportional control. The proposed system investigates the relationship between forearm electrical activity and forces exerted by the fingertips. This system is used to calculate the muscular force while lifting, pulling, pushing the object in Hot Rolling Mill with the help of electromyography. The load cell is used to calculate the force exerted by the fingertips of the human. The value of the force exerted is displayed by using the LCD. A threshold force value is fixed and it is compared with the actual force exerted by the human being. If the actual force exceeds the threshold value human beings will be affected in a way like sprain and bone rubbing etc., so an alarm is provided to indicate this situation to avoid the above mentioned accidents. The proposed system is simulated by using keil C and the simulated results are verified.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of Force in Human Muscle Using EMG in Hot Rolling Mill

Govindaraju¹

2015

AJSS

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“…EMG signals recorded noninvasively from the skin surface are referred to as surface EMG (sEMG) signals, whereas the signals recorded invasively directly from the muscles are referred to as intramuscular EMG (iEMG) signals. Both sEMG and iEMG signals are widely used in a range of clinical, diagnostic, and rehabilitative applications [2].…”

Section: Introductionmentioning

confidence: 99%

Variational mode decomposition for surface and intramuscular EMG signal denoising

Ashraf

Shafiq

Sajjad

et al. 2023

Biomedical Signal Processing and Control

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“…The nervous system controls the motor units generating EMG signals. The recorded EMG, also known as a myoelectric signal, is used for rehabilitation, prosthetic, and clinical diagnostic applications [1]. Concentric needle electrodes or surface electrodes are used to acquire and record EMG signals.…”

Section: Introductionmentioning

confidence: 99%

Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

Ashraf¹,

Waris²,

Gilani³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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Empirical Mode Decomposition (EMD) is a data-driven and fully adaptive signal decomposition technique to decompose a signal into its Intrinsic Mode Functions (IMF). EMD has attained great attention due to its capabilities to process a signal in the frequency-time domain without altering the signal into the frequency domain. EMD-based signal denoising techniques have shown great potential to denoise nonlinear and nonstationary signals without compromising the signal's characteristics. The denoising procedure comprises three steps, i.e., signal decomposition, IMF thresholding, and signal reconstruction. Thresholding is performed to assess which IMFs contain noise. In this study, Interval Thresholding (IT), Iterative Interval Thresholding (IIT), and Clear Iterative Interval Thresholding (CIIT) techniques have been explored for denoising of electromyography (EMG) signals. The effect of different thresholding operators, i.e., SOFT, HARD, and Smoothly Clipped Absolute Deviation (SCAD), on the performance of EMD-based EMG denoising techniques is also investigated. 15 EMG signals, recorded from the upper limb of 5 healthy subjects, were used to identify the best possible combination of thresholding technique and thresholding operator for denoising EMG signals. The performance of denoising techniques is assessed by calculating the Signal to Noise (SNR) ratio of the signals. The results are further evaluated using a two-way Analysis of Variance (ANOVA) statistical test. The results demonstrated that the mean SNR values yielded by the IIT thresholding technique outperform the IT thresholding technique (P-value < 0.05), but there is no significant difference in mean SNR values of IIT and CIIT techniques (P-value = 0.9951). For thresholding operators, there is no significant difference in mean SNR values of the HARD and SOFT operator (P-value = 0.0968), whereas the HARD operator outperforms SCAD (P-value < 0.05). It has also shown that the combination of IIT thresholding with SOFT operator and threshold value equal to half of the universal threshold denoise the EMG signals while preserving the original signal's characteristics. IIT-based EMD denoising technique with HARD thresholding operator yields the highest SNR, irrespective of the level of noise embedded in the signal. Whereas IIT with SOFT operator provides comparable SNR and successfully preserves the shape of EMG signals. The

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Evaluating the Electromyographical Signal During Symmetrical Load Lifting

Cited by 4 publications

References 43 publications

Analysis of Force in Human Muscle Using EMG in Hot Rolling Mill

Analysis of Force in Human Muscle Using EMG in Hot Rolling Mill

Variational mode decomposition for surface and intramuscular EMG signal denoising

Threshold Parameters Selection for Empirical Mode Decomposition-Based EMG Signal Denoising

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