A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study
Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver’s state. To the best of our knowledge, th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/17/7387 |
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| author | Walaa Othman Batol Hamoud Alexey Kashevnik Nikolay Shilov Ammar Ali |
| author_facet | Walaa Othman Batol Hamoud Alexey Kashevnik Nikolay Shilov Ammar Ali |
| author_sort | Walaa Othman |
| collection | DOAJ |
| description | Driving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver’s state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver’s maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver’s stable state when aggressive external events or maneuvers occur. |
| first_indexed | 2024-03-10T23:13:55Z |
| format | Article |
| id | doaj.art-ee43d35bc0734924bf62a177f49dbf11 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T23:13:55Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ee43d35bc0734924bf62a177f49dbf112023-11-19T08:49:12ZengMDPI AGSensors1424-82202023-08-012317738710.3390/s23177387A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case StudyWalaa Othman0Batol Hamoud1Alexey Kashevnik2Nikolay Shilov3Ammar Ali4Saint Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, RussiaSaint Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, RussiaSaint Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, RussiaSaint Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, RussiaInformation Technology and Programming Faculty, ITMO University, 191002 St. Petersburg, RussiaDriving behaviour analysis has drawn much attention in recent years due to the dramatic increase in the number of traffic accidents and casualties, and based on many studies, there is a relationship between the driving environment or behaviour and the driver’s state. To the best of our knowledge, these studies mostly investigate relationships between one vital sign and the driving circumstances either inside or outside the cabin. Hence, our paper provides an analysis of the correlation between the driver state (vital signs, eye state, and head pose) and both the vehicle maneuver actions (caused by the driver) and external events (carried out by other vehicles or pedestrians), including the proximity to other vehicles. Our methodology employs several models developed in our previous work to estimate respiratory rate, heart rate, blood pressure, oxygen saturation, head pose, eye state from in-cabin videos, and the distance to the nearest vehicle from out-cabin videos. Additionally, new models have been developed using Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to classify the external events from out-cabin videos, as well as a Decision Tree classifier to detect the driver’s maneuver using accelerometer and gyroscope sensor data. The dataset used includes synchronized in-cabin/out-cabin videos and sensor data, allowing for the estimation of the driver state, proximity to other vehicles and detection of external events, and driver maneuvers. Therefore, the correlation matrix was calculated between all variables to be analysed. The results indicate that there is a weak correlation connecting both the maneuver action and the overtaking external event on one side and the heart rate and the blood pressure (systolic and diastolic) on the other side. In addition, the findings suggest a correlation between the yaw angle of the head and the overtaking event and a negative correlation between the systolic blood pressure and the distance to the nearest vehicle. Our findings align with our initial hypotheses, particularly concerning the impact of performing a maneuver or experiencing a cautious event, such as overtaking, on heart rate and blood pressure due to the agitation and tension resulting from such events. These results can be the key to implementing a sophisticated safety system aimed at maintaining the driver’s stable state when aggressive external events or maneuvers occur.https://www.mdpi.com/1424-8220/23/17/7387correlation analysisvital signsmachine learningdriving behaviourdriver maneuversexternal events |
| spellingShingle | Walaa Othman Batol Hamoud Alexey Kashevnik Nikolay Shilov Ammar Ali A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study Sensors correlation analysis vital signs machine learning driving behaviour driver maneuvers external events |
| title | A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study |
| title_full | A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study |
| title_fullStr | A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study |
| title_full_unstemmed | A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study |
| title_short | A Machine Learning-Based Correlation Analysis between Driver Behaviour and Vital Signs: Approach and Case Study |
| title_sort | machine learning based correlation analysis between driver behaviour and vital signs approach and case study |
| topic | correlation analysis vital signs machine learning driving behaviour driver maneuvers external events |
| url | https://www.mdpi.com/1424-8220/23/17/7387 |
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