Real-time monitoring of cognitive load is a key topic in the prevention of workplace injuries and in the assessment of operator well-being in high-intensity contexts. The final objective of this study is to develop a hardware and software tool capable of acquiring, processing and analyzing the electrodermal activity (EDA) signal, to detect different states of cognitive workload (CWL). The hardware system consisted of safety helmet where some extra instrumentation have been installed: a Raspberry Pi 5, to which a camera and an accelerometer were connected, and a Shimmer GSR 3+ sensor, which was used for the detection of the EDA signal.