Detection technique of muscle activation intervals for sEMG signals based on the Empirical Mode Decomposition

The best way to detect the onset and offset time of muscle activation is through visual decision making by clinical experts like physical therapists. Humans can recognize muscle activation trends recorded from surface EMG signals. Current computer-based algorithms are being researched toward yielding similar results by clinical experts. A new algorithm in this paper has the ability, like humans, to recognize a trend from noisy input signals. We propose using the Empirical Mode Decomposition (EMD), because it is effectual to recognize trends which are decomposed by Hilbert transform and synthesized of Intrinsic Mode Functions (IMFs). These synthesized functions represent hidden low-frequency trends according to more iterative processes. Iterations will be stopped at the minimum SD of a resting period of EMG signals. The proposed method is very useful and easy implemented, but there are some limitations. The EMD method is only available on an off-line data and requires relatively high computational performances to find the IMFs. To use the proposed method, it is possible to detect muscle activation intervals of sEMG signals.