| 1. |
- Rahman, Hamidur, Doctoral Student, 1984-
(författare)
-
Artificial Intelligence for Non-Contact-Based Driver Health Monitoring
- 2021
-
Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- In clinical situations, a patient’s physical state is often monitored by sensors attached to the patient, and medical staff are alerted if the patient’s status changes in an undesirable or life-threatening direction. However, in unsupervised situations, such as when driving a vehicle, connecting sensors to the driver is often troublesome and wired sensors may not produce sufficient quality due to factors such as movement and electrical disturbance. Using a camera as a non-contact sensor to extract physiological parameters based on video images offers a new paradigm for monitoring a driver’s health and mental state. Due to the advanced technical features in modern vehicles, driving is now faster, safer and more comfortable than before. To enhance transport security (i.e. to avoid unexpected traffic accidents), it is necessary to consider a vehicle driver as a part of the traffic environment and thus monitor the driver’s health and mental state. Such a monitoring system is commonly developed based on two approaches: driving behaviour-based and physiological parameters-based.This research work demonstrates a non-contact approach that classifies a driver’s cognitive load based on physiological parameters through a camera system and vehicular data collected from control area networks considering image processing, computer vision, machine learning (ML) and deep learning (DL). In this research, a camera is used as a non-contact sensor and pervasive approach for measuring and monitoring the physiological parameters. The contribution of this research study is four-fold: 1) Feature extraction approach to extract physiological parameters (i.e. heart rate [HR], respiration rate [RR], inter-beat interval [IBI], heart rate variability [HRV] and oxygen saturation [SpO2]) using a camera system in several challenging conditions (i.e. illumination, motion, vibration and movement); 2) Feature extraction based on eye-movement parameters (i.e. saccade and fixation); 3) Identification of key vehicular parameters and extraction of useful features from lateral speed (SP), steering wheel angle (SWA), steering wheel reversal rate (SWRR), steering wheel torque (SWT), yaw rate (YR), lanex (LAN) and lateral position (LP); 4) Investigation of ML and DL algorithms for a driver’s cognitive load classification. Here, ML algorithms (i.e. logistic regression [LR], linear discriminant analysis [LDA], support vector machine [SVM], neural networks [NN], k-nearest neighbours [k-NN], decision tree [DT]) and DL algorithms (i.e. convolutional neural networks [CNN], long short-term memory [LSTM] networks and autoencoders [AE]) are used. One of the major contributions of this research work is that physiological parameters were extracted using a camera. According to the results, feature extraction based on physiological parameters using a camera achieved the highest correlation coefficient of .96 for both HR and SpO2 compared to a reference system. The Bland Altman plots showed 95% agreement considering the correlation between the camera and the reference wired sensors. For IBI, the achieved quality index was 97.5% considering a 100 ms R-peak error. The correlation coefficients for 13 eye-movement features between non-contact approach and reference eye-tracking system ranged from .82 to .95.For cognitive load classification using both the physiological and vehicular parameters, two separate studies were conducted: Study 1 with the 1-back task and Study 2 with the 2-back task. Finally, the highest average accuracy achieved in terms of cognitive load classification was 94% for Study 1 and 82% for Study 2 using LR algorithms considering the HRV parameter. The highest average classification accuracy of cognitive load was 92% using SVM considering saccade and fixation parameters. In both cases, k-fold cross-validation was used for the validation, where the value of k was 10. The classification accuracies using CNN, LSTM and autoencoder were 91%, 90%, and 90.3%, respectively. This research study shows such a non-contact-based approach using ML, DL, image processing and computer vision is suitable for monitoring a driver’s cognitive state.
|
|
| 2. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(författare)
-
Deep Learning in Remote Sensing : An Application to Detect Snow and Water in Construction Sites
- 2021
-
Ingår i: Proceedings - 2021 4th International Conference on Artificial Intelligence for Industries, AI4I 2021. - 9781665434102 ; , s. 52-56
-
Konferensbidrag (refereegranskat)abstract
- It is important for a construction and property development company to know weather conditions in their daily operation. In this paper, a deep learning-based approach is investigated to detect snow and rain conditions in construction sites using drone imagery. A Convolutional Neural Network (CNN) is developed for the feature extraction and performing classification on those features using machine learning (ML) algorithms. Well-known existing deep learning algorithms AlexNet and VGG16 models are also deployed and tested on the dataset. Results show that smaller CNN architecture with three convolutional layers was sufficient at extracting relevant features to the classification task at hand compared to the larger state-of-the-art architectures. The proposed model reached a top accuracy of 97.3% in binary classification and 96.5% while also taking rain conditions into consideration. It was also found that ML algorithms,i.e., support vector machine (SVM), logistic regression and k-nearest neighbors could be used as classifiers using feature maps extracted from CNNs and a top accuracy of 90% was obtained using SVM algorithms.
|
|
| 3. |
- Rahman, Hamidur, Doctoral Student, 1984-, et al.
(författare)
-
Vision-based driver’s cognitive load classification considering eye movement using machine learning and deep learning
- 2021
-
Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:23
-
Tidskriftsartikel (refereegranskat)abstract
- Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drivers’ unsafe behaviors. Therefore, to make the traffic environment safe it is important to keep the driver alert and awake both in human and autonomous driving cars. A driver’s cognitive load is considered a good indication of alertness, but determining cognitive load is challenging and the acceptance of wire sensor solutions are not preferred in real-world driving scenarios. The recent development of a non-contact approach through image processing and decreasing hardware prices enables new solutions and there are several interesting features related to the driver’s eyes that are currently explored in research. This paper presents a vision-based method to extract useful parameters from a driver’s eye movement signals and manual feature extraction based on domain knowledge, as well as automatic feature extraction using deep learning architectures. Five machine learning models and three deep learning architectures are developed to classify a driver’s cognitive load. The results show that the highest classification accuracy achieved is 92% by the support vector machine model with linear kernel function and 91% by the convolutional neural networks model. This non-contact technology can be a potential contributor in advanced driver assistive systems.
|
|
