Smartwatch-Based Driver Vigilance Indicator With Kernel-Fuzzy-C-Means-Wavelet Method

Abstract

A high-precision driver vigilance predictor could be a monetary countermeasure to reduce road accidents. Heart rate variability is a well-known measurement parameter to predict driver vigilance state, but the measurement is susceptible to motion artifact due to body movement where the electrocardiogram (ECG) sensor device had to be worn close to the heart. Thus, this paper presents a novel approach to measure the ECG from the driver palms while holding on the steering wheel. In addition, photoplethysmograms sensor attached on a driver finger can also measure the similar heart rate pattern, known as pulse rate variability. Another significant vigilance measurement parameter, respiratory rate variability, can be derived directly from the ECG with the squaring baseline method, without the usage of respiratory sensor. Furthermore, this paper is also focusing on the integration of age and gender as vigilance measurement parameter as each individual exhibits distinct signal pattern. Autonomous rules are derived from the data set that performs the kernel fuzzy c-means with if-then rules extraction, which subsequently classify the driver vigilance level into two predefined classes, that are drowsy and awake. The vigilance monitoring application is developed in smartwatch, able to perform the features extraction, and then predict the driver vigilance class based on the Kernel Fuzzy-C-Mean trained model. A vibration warning will be triggered to the driver if the driver is estimated as drowsy in five consecutive time frames. In fact, the experimental results stated that the prediction accuracy can be achieved at 97.28% on average across variant subjects.