Abstract
The assessment of muscle-recruitment timing from electromyography (EMG) signal 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 muscle-activation 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 and 105 real surface EMG 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 <inline-formula> <tex-math notation="LaTeX">$\approx 10$ </tex-math></inline-formula> ms) and real datasets (MAE < 30 ms), minimally affected by the SNR variability and compatible with the timing of EMG-driven assistive devices. Concomitantly, the maximum frequency of the activations is computed, ranging from around 100 Hz up to almost 500 Hz. This suggests 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.
Highlights
Muscle activity is typically quantified during human movement using surface electromyography. sEMG is a non-invasive approach that permits the attainment of many different tasks, such as estimating muscle function, evaluating muscular fatigue, identifying movement patterns, explaining neuromotor-system strategies in complex tasks, controlling sEMG-driven assistive devices, and so on [1,2,3]
SIMULATED sEMG SIGNALS Mean performances of onset and offset detection timing in simulated signals are shown in Table I; mean average error (MAE) and bias, precision, recall, and F1-score are computed
Variability of MAE in function of α, σ, and signal-to-noise ratio (SNR) is quantified in Table II and III for onset and offset, respectively
Summary
Muscle activity is typically quantified during human movement using surface electromyography (sEMG). sEMG is a non-invasive approach that permits the attainment of many different tasks, such as estimating muscle function, evaluating muscular fatigue, identifying movement patterns, explaining neuromotor-system strategies in complex tasks, controlling sEMG-driven assistive devices, and so on [1,2,3]. Myoelectric non-pattern-recognition-based prosthetic control is acknowledged to include an activation-timing algorithm as one of the fundamental steps of the control procedure [10] To this aim, the assessment of the onset event is more crucial than identifying the offset event. The approach of myoelectric pattern-recognitioncontrolled devices typically mimics the functionality of an actual limb, employing EMG signals from residual muscles leftover after amputation or congenital disability [11] This approach presupposes a reliable acquisition and preprocessing of sEMG signals, a focused feature-extraction procedure, an accurate signal-classification process to predict a subset of designated motor tasks, and eventually a multifunction prosthesis control. Implementing an onset-detection algorithm could help since it has been shown that the performance of the classification phase could be improved by windowing sEMG signals using a reliable assessment of activation events [13]
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