Machine Learning Based Driver Emotion Monitoring for Vehicular IoT

In recent years, the convergence of driver monitoring systems (DMS), cloud technologies, and self-driving cars has gained increasing attention. Driver monitoring systems use a variety of camera and sensor technologies to detect the driver's state in real time, including fatigue levels, and stress levels based on emotional state. Such systems help to improve driving safety and reduce accidents caused by driver fatigue or emotional high pressure. This paper proposes an in-vehicle driver emotion recognition cloud computing scheme. Contact sensors are used in conjunction with a steering wheel to collect the electrical skin signals from the driver's palm and transmit the data to a cloud server via a 6G IoT device. The driver's emotional state such as fatigue, alertness, and stress level is measured by One-Class Support Vector Machines (OCSVM) combined with a Long Short-Term Memory (LSTM) recurrent neural network. When low mood, drowsiness, or lack of alertness are recognized, the driver is reminded and given feedback on safe driving through methods such as seat vibration and voice prompts. And the vehicle's automatic driving assistance mode will be turned on in extreme situations. The proposed method combines unsupervised learning with threshold-based wavelet denoising, effectively removing noise data generated by motion during the collection of human electrodermal signals. Experimental results demonstrate that the motion artifact removal algorithm presented in this paper exhibits superior denoising effects. Compared to traditional filtering algorithms, the SNR(Signal to Noise Ratio) is enhanced by 4.819 dB, while the RMSE(Root Mean Square Error) is reduced by 0.0385. Ultimately, the accuracy is improved by 2.44% compared to conventional emotion recognition methods.