The paper presents a wearable system able to evaluate real time the risk of fall in elderly people, promoting the fast adoption of properly intervention strategies for reducing injuries (e.g. by activating an impact reduction system). A wireless and minimally invasive surface Electromyography-based system (EMG) has been used to measure four lower limb muscles activities. This work deals with the identification of highly discriminative features extracted from the EMG signals for the automatic detection of people instability. The framework prototype uses a threshold-based approach assuring real time functioning and permitting the detection of a typical imbalance condition about 200ms after the stimulus perturbation, in simulated and controlled fall conditions.