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.