Collaborative robotics plays a significant role in the industrial sector, especially following the advent of the 4th and 5th industrial revolutions. In this context, humans and robots share a workspace where they collaborate and exchange information, enhancing each other’s strengths. Robots perform repetitive tasks with precision and speed, while humans provide essential decision-making capabilities, ensuring an effective production process. However, guaranteeing the safety of human-robot interaction is crucial, a concept known as "safety collaboration". To achieve this, robots must recognize human activities, such as detecting abrupt movements, and respond accordingly. The recognition needs to be rapid to make the safety system activating as quickly as possible to prevent collisions.  The objective of this study was to detect abrupt movements in real time using data from magneto-inertial measurement units (MIMUs) and an artificial intelligence network.