Within muscle activity based on surface electromyographic (EMG) signals, the percentage estimate of muscle activation, which is the level of intensity with which muscles have been activated, has not been exploited. This work presents a system that allows the estimation of different movements, for this case five hand gestures, where the EMG signals are obtained by means of a Myo armband. For the estimation, discrimination of sensors to be used is carried out according to the intensity of activity presented and the gesture made, recognized by a multi-channel convolutional neural network. Likewise, the EMG envelope is obtained to perform the estimation, evaluating two methods, root-mean-square (RMS) and signal filtering using a Butterworth filter. According to the results, the system managed to recognize all the gestures made in real-time, as well as the effective percentage estimation of muscle activity, with a minimum of 50% for stable force and 22% for incremental force, that is when the signal has been reduced so much that it moves to a neutral gesture. In general, the implemented system can be used in any type of gesture recognition application to improve its recognition, or even in rehabilitation exercises that require showing the progress in muscle activity in different movements.