Development of a real-time in car physiological acquisition system for monitoring attention drifting phenomenon - Zaid, Zulhilmi (2021)
Road accidents is ranked as the fifth cause of death in Malaysia. Every year, almost 7,000 deaths were reported due to road accidents. The long-standing reason for cause of accidents is lost of attention. Lost of attention, also known as habituation, is highly undesirable while performing tasks which requires concentration for a long period of time such as long distance driving. There are several factors that contribute to habituation, such as familiar or monotonous driving routes and fatigue. As the number of accidents have reached to a number that concerns Malaysian Institute of Road Safety Research (MIROS), which is almost 7,000 deaths were reported, many solutions were suggested in order to tackle the growing issue. Hence, this research is to establish an objective of direct measurement method using encephalogram (EEG) to detect the occurrence of habituation while driving then to alert the driver. The development of this system involves the development of a real-time signal processing algorithm to acquire event related potentials (ERPs) from EEG for the detection of habituation; develop an in-car biosensors monitoring system and efficient data acquisition. Where most of the methods on detecting loss of attention are indirect, EEG provides a direct measurement of the level alertness of a person. This research is a significant endeavor in measuring the level of attention during driving as it capture the moment when the attention level begins to drift in the ERPs from the EEG signal in real-time. A low-power consumption of an EEG signal acquisition system has been designed to record the electrical activity of the brain non-invasively. The system consists of an amplifier with a bandwidth of 0.13 Hz – 12 Hz which was designed using low-power instrumentation amplifier AD620, low power and high gain operational amplifier CA3130. The resulting gain ranges from 20 μV to 5 V. The amplified acquired signal is then filtered using a notch filter to remove the power signal interference. The signal then goes into Arduino Due. Here the signal is segmented into 500 single-trials of 500 ms for further noise removal using the averaging technique. The output will be used to check on the habituation scale, known as Attention Degrading Scale (ADS). The scale is based on the threshold value obtained from fuzzy-rule based system, which is the value towards the negative threshold inteprets that the amplitude difference of N100 ERP component waveform decreased that brought the definition of the attention is degrading. The resulting signal is used for habituation detection.