The Functional Electrical Stimulation (FES) exploits low-energy electrical pulses to retrain, or even restore, the functional mobility in patient affected by neuromuscular disorders. The FES effectiveness can be increased modulating the stimulation delivery using the activation pattern of muscles. The ATC is an event-driven processing technique which can be applied to the Surface ElectroMyoGraphy (sEMG) to estimate the muscle contraction force with a low-power approach, representing an effective solution for controlling the FES therapy. The aim of this project is the optimization of an embedded ATC-controlled FES system, which was developed using Python programming language. The application of the system relies on two calibration phases: the first calibration determines the maximum ATC value expressed during the muscle contraction, whereas in the second phase stimulation parameters are tuned to induce the execution of functional movements.