Industrial workplaces that require continuous concentration and physical effort often see accidents triggered by elevated cognitive workload, mental fatigue, and stress. To detect these early precursors, the present thesis introduces a sensorized safety helmet that combines various physiological and behavioural sensors into a single, wearable device. The helmet incorporates electroencephalography (EEG) for neural activity, electrodermal activity (EDA) for arousal variations, photoplethysmography (PPG) for cardiovascular response, an inertial measurement unit (IMU) for head posture and sudden movements, and a camera for oculometric measures. All modules are contained in lightweight customized cases mounted in a commercially available industrial helmet, while a Raspberry Pi 5 processor acquires every signal and runs real-time warnings.