Non-Interference Driving Fatigue Detection System Based on Intelligent Steering Wheel

Driving fatigue is an important factor leading to traffic accidents. For this reason, we propose a non-interference fatigue detection system, which consists of a steering wheel embedded with an electrocardiogram (ECG) acquisition device and an ECG fatigue detection model. By holding the steering wheel with the driver's palm, the system can collect their ECG signals and transmit them to the fatigue detection model for tiredness analysis after preprocessing. In particular, the proposed ECG fatigue detection model is composed of a simulation generation module based on a cycle-generative adversarial network (CycleGAN) and a fatigue detection module based on a fuzzy convolution neural network (FCNN). The acquired palm signal is fed into the simulation generation module to generate a clearer chest-like signal, thereby improving the final task performance. In addition, a new FCNN is posed to analyze the simulated chest signal to focus on the time variation and ignore the specificity of the ECG signal, therefore increasing the robustness of the system. The experimental results show that the proposed fatigue detection model has good stability and accuracy.

Automated summary

Driving fatigue is a significant factor in traffic accidents. Researchers have proposed a non-interference fatigue detection system that utilizes an electrocardiogram (ECG) acquisition device embedded in the steering wheel. By collecting the driver's ECG signals through their palm, the system can analyze tiredness after preprocessing. The system consists of a simulation generation module based on a cycle-generative adversarial network (CycleGAN) and a fatigue detection module based on a fuzzy convolution neural network (FCNN). Experimental results demonstrate the stability and accuracy of the proposed fatigue detection model.