Humanoid robots are currently still far from reaching the impressive human walking capabilities. Among the different methods used to design walking controllers, those based on the Zero-Moment Point (ZMP) criterion are among the most popular, even if they induce intrinsic limitations in terms of energy consumption and robustness. In parallel, bio-inspired controllers are emerging. They overcome the ZMP-based limitations, but still miss robust stabilization rules to be validated on real robots. This contribution studies how to efficiently compute the ZMP in realtime on a robot walking with bio-inspired control rules, in order to detect when the robot stability is compromised.