Recognition of Drivers’ Activity Based on 1D Convolutional Neural Network

Abstract

Background and objective: Driving a car is a complex activity which involves movements of the whole body. Many studies on drivers’ behavior are conducted to improve road traffic safety. Such studies involve the registration and processing of multiple signals, such as electroencephalography (EEG), electrooculography (EOG) and the images of the driver’s face. In our research, we attempt to develop a classifier of scenarios related to learning to drive based on the data obtained in real road traffic conditions via smart glasses. In our approach, we try to minimize the number of signals which can be used to recognize the activities performed while driving a car. Material and methods: We attempt to evaluate the drivers’ activities using both electrooculography (EOG) and a deep learning approach. To acquire data we used JINS MEME smart glasses furnished with 3-point EOG electrodes, 3-axial accelerometer and 3-axial gyroscope. Sensor data were acquired on 20 drivers (ten experienced and ten learner drivers) on the same 28.7 km route under real road conditions in southern Poland. The drivers performed several tasks while wearing the smart glasses and the tasks were linked to the signal during the drive. For the recognition of four activities (parking, driving through a roundabout, city traffic and driving through an intersection), we used one-dimensional convolutional neural network (1D CNN). Results: The maximum accuracy was 95.6% on validation set and 99.8% on training set. The results prove that the model based on 1D CNN can classify the actions performed by drivers accurately. Conclusions: We have proved the feasibility of recognizing drivers’ activity based solely on EOG data, regardless of the driving experience and style. Our findings may be useful in the objective assessment of driving skills and thus, improving driving safety.

The Relationship between Stress Levels Measured by a Questionnaire and the Data Obtained by Smart Glasses and Finger Pulse Oximeters among Polish Dental Students

“…The differences between the power of total linear acceleration and total angular velocity before the task and during the task were statistically significant; however, we did not observe such differences for the power of the total EOG signal. This finding proves that the eyeball movements may not be correlated with the fact of performing the task, contrary to the findings of studies conducted by Doniec et al [14,76], Li et al [77], Shirahama et al [78], and Deng et al [79].…”

“…The problem of extraction and selection of appropriate features from EOG signals in terms of the recognition of actions has not been thoroughly investigated. EOG signals acquired with smart glasses contain lots of data, but the recognition of several cognitive activities (e.g., learning, relaxing, or stress) is challenging [14,76,78]. Meina et al,in [6] proved that the perceived stress level and physiological signals are strongly correlated based on the results of Welch's t-test.…”

Driving Fatigue Detection with Three Non-Hair-Bearing EEG Channels and Modified Transformer Model

“…Some typical deep learning algorithms are used in comparison with our model for one-dimensional feature vector classification, including one-dimensional CNN (1D CNN) [ 55 ], LSTM [ 56 ], Bidirectional LSTM(BiLSTM) [ 57 ], Gate Recurrent Unit (GRU) [ 58 ], Bidirectional GRU (BiGRU) [ 59 ]. Brief descriptions for each baseline are as follows.…”

Explainable Convolutional Neural Network to Investigate the Age-Related Changes in Multi-Order Functional Connectivity

“…With advancements in science and medical technologies, the average life span of humans has gradually increased [29]. Therefore, there is a growing need for brain-computer interface (BCI) systems for healthy elderly persons going through nonpathological physical and cognitive declines [30,52].…”