Mental load detection method and device

Abstract

The embodiment of the invention provides a mental load detection method and device. The method comprises the following steps of: acquiring a physiological signal of a to-be-detected object; inputtingthe physiological signal into a preset time convolution network model, and obtaining a mental load type of the to-be-tested object according to an output result of the time convolution network model,wherein the physiological signal comprises an electroencephalogram signal, and the time convolution network model is obtained after training according to a physiological signal sample with a mental load type label. The physiological signals are input into the preset time convolution network model, so that the preset time convolution network model is obtained after training according to the physiological signal sample with the mental load type, and the recognition result of the mental load type can be output, so that the detection process is quick and accurate, the calculation consumption is lower, and meanwhile, the redundant information can be automatically eliminated by utilizing the time convolution network model.

Description

technical field [0001] The invention relates to the field of mental load identification, in particular to a mental load detection method and device. Background technique [0002] In the past ten years, mental workload detection has gradually become a research hotspot in academia and industry. Moderate mental load can improve work efficiency, while excessive mental load can affect human health and cause major safety accidents. Therefore, the detection of mental load is crucial to mental health. [0003] The traditional mental load test requires the subject to fill in the scale, which is too subjective and relies on the honesty of the subject. The mental load recognition method based on physiological signal measurement is of great significance. The current physiological signal fusion mental load detection method mainly includes the following steps: artificially define the characteristics of different physiological signals; use feature engineering to realize information fusio...

AI Technical Summary

Problems solved by technology

[0004] The effect of the current mental load detection method based on physiological signals depends entirely on the effectiveness of artificially defined features, and manually defined features cannot consider redundant information between different physiological signals, so the accuracy of the above method is poor

Images