The paper presents an online estimation of parameters of a multi-input renewal Markov process. The underlying model is derived from the physiological generation of intramuscular electromyographic (iEMG) signals, which are recorded by wire electrodes. The iEMG is the sum of several sparse spikes trains and noise. An hidden Markov model, whose parameters express the muscular activity, is developed. The time duration between spikes is modeled with a discrete Weibull distribution, helping us to reduce the complexity of the estimation done with the help of a Bayes filter.