To establish a technology for rapid recognition of fear emotions in order to facilitate the monitoring and interventions of fear emotions among personnel doing unusual jobs based on the fNIRSNet model.

Functional near infrared spectroscopy (fNIRS) was used to record the changes of cerebral blood oxygen activities in 50 subjects watching non-thrilling and thrilling videos, whose emotions were assessed based on Sel f-Assessment Manikin (SAM). Signals of blood oxygen in response to the two types of videos were labeled and preprocessed. Channels with significant differences were identified by calculating the Beta value. The features of data on fNIRS in the activated region of the brain where the channels were located were extracted. The fNIRSNet algorithm was used to establish a fear emotion recognition model, and the accuracy was evaluated using the 50-fold cross-validation method.

The SAM showed that fear emotions were induced, and the brain region activated by fear emotions was located in the medial prefrontal cortex. The fNIRSNet algorithm could help classify fear emotions with high precision by analyzing the data on the 20 s short time series, with an accuracy of 82.36%.

This technology for emotion recognition based on the fNIRSNet model is capable of rapid and accurate assessment of fear emotions, which can be used for related monitoring and interventions.