Wavelet-Based Assessment of the Muscle-Activation Frequency Range by EMG Analysis

Abstract

The assessment of muscle-recruitment timing from surface EMG signal (sEMG) is relevant in different fields, including clinical gait analysis and robotic systems to interpret user's motion intention. However, available methods typically provide only information in time domain without evaluating muscleactivation frequency content. This study aims to propose a novel adaptative algorithm for detecting muscle activation in time-frequency domain based on continuous wavelet transform (CWT) analysis. Precisely, the novel contribution of the proposed algorithm consists of evaluating the frequency range of every muscle activations detected in time domain. Performances are evaluated on a test bench of 720 simulated sEMG signals and 105 real sEMG signals, stratified for signal-to-noise ratio (SNR), and then validated against different reference algorithms. Outcomes indicate that the proposed approach can provide an accurate prediction of muscle onset and offset timing in both simulated (mean absolute error, MAE ≈ 10 ms) and real datasets (MAE < 30 ms), minimally affected by the SNR variability and compatible with the timing of sEMGdriven assistive devices. Concomitantly, the maximum frequency of the activations is computed, ranging from around 100 Hz up to almost 500 Hz. This suggest a large within-muscle between-muscle variability of the frequency range. In conclusion, the current study introduces a novel reliable wavelet-based algorithm to detect both time and frequency content of muscle activation, suitable in different conditions of signal quality.INDEX TERMS surface EMG signal, onset-offset detection, muscle activation, time-frequency domain, wavelet transform.

Muscle Co-Contraction Detection in the Time–Frequency Domain

“…Furthermore, the mean error with respect to the ground truth (as defined in Section 2.3) was computed in terms of AE and TD (±SD) (Figure 4). These results of the assessment of the co-contraction between two muscles are comparable with those achieved for the detection of the activation of a single muscle in the aforementioned recent studies [20,21,29,36,41]. However, it is worth highlighting that each one of the aforementioned studies reported the performance of the algorithm in the assessment of the activity of a single muscle.…”

“…In the present study, CWT coscalogram function in the time-frequency domain of denoised sEMG signals was adopted for assessing the co-contraction signal between the selected muscles. Co-contraction timing was computed in a single stride as the beginning (onset) and the end (offset) of the time interval when the coscalogram function surpassed 1% of the cross-energy-density peak in the selected stride [29]. Once the co-contraction interval was detected in the time domain, the correspondent co-contraction content in the frequency domain was computed as the frequency range associated with the coscalogram function to that specific time interval.…”

Intelligent Systems for Muscle Tracking: A Review on Sensor‐Algorithm Synergy

“…However, the advantage of WT lies especially in nonisometric movements. Nardo et al [ 100 ] made use of WTs to assess the frequency range of every nonisometric muscle activation detected by sEMG. In addition, WTs have been found to reflect signal components relating to the activities of variable‐speed muscle fibers as well as the muscle timing characteristics, making them ideal for optimizing training protocols for rehabilitation.…”

Medical system based on thermal optical system and neural network

“…The researchers emphasized the proposed system gave high accuracy and that the system is light in weight and small in size. Nardo et al [13] used continuous frequency domain waveform (CWT) analysis to detect muscle activity. As a practical application of the integration between engineering and medical information, the researchers directed to study the understanding of the relationship between falsehood and the disturbance of cardiac signals.…”

“…Tests were conducted on several students while playing to assess the different cases of truth and deception. The studies then developed to suggest a methodology for detecting lies by detecting the change in the blink of an eye [13]- [16].…”