## 2 Related Work
### Approaches Based on Machine Learning  
[PERSON] et al., in 2011, identified mobile usage as one of the major causes of driving accidents [16]. They implemented a hidden conditional random fields model based on mouth, facial, and hand features for profiling of cell phone usage by the drivers. They were able to achieve 91.2% accuracy. [PERSON] et al. proposed a feature-based approach using contourlet transform (CT), skin-like region segmentation, and homomorphic filtering using a random forest classifier for detecting distraction [17]. Their approach was developed for classifying four activities, namely: operating the shift gear, grasping the steering wheel,eating, and talking on a mobile phone. It was empirically observed that eating was the most difficult category to classify. Their approach yielded 88% accuracy on their self-generated dataset at Southeast University (SEU). [PERSON] et al. proposed an approach based on Haar-like features to detect the driver's eye state and head movement, exhibiting 80% accuracy [18]. Image classification approaches are usually computationally intensive. To address this issue, [PERSON] and [PERSON] presented an architecture which used FPGA for faster image processing [19]. Their objective was to determine if the drivers' eyes were closed in the image. They combined grayscale projection with Prewitt operator-based edge detection for the classification. [PERSON] et al. implemented various features including facial features, eye gaze direction, and head pose estimation on a Raspberry Pi [20]. They used these features along with a support vector machine for distraction detection. [PERSON] et al. proposed a real-time system using the driver's head and eye movement for detecting cognitive distraction [21]. They proposed a semi-supervised method to increase the time efficiency of labeling the training data. [PERSON] et al. proposed a framework using histogram of gradients along with adaptive boosting, and yielded 93.9% accuracy on their self-generated dataset [22]. [PERSON] et al. proposed a distraction detection system based on five visual cues using a random forest classifier and achieved an accuracy of 82.78% [23]. These cues included eye closure, arm position, eye gaze, orientation, and facial expression. [PERSON] et al. proposed a real-time algorithm for detection of cognitive distraction using a support vector machine (SVM) [24]. They evaluated their approach on self-generated datasets, achieving an accuracy of 98.5% and 93.0% for urban and highway simulated scenarios, respectively. [PERSON] et al. proposed an approach based on random forest and contourlet transform for distraction detection [25]. They tested their approach on a dataset that was generated using the driver's side pose and achieved an accuracy of 90.5%. [PERSON] et al. [26] presented a two-pronged approach in which the system first determines if a driver was distracted, based on yawing, head position, eye position, mouth position, etc. In the case of distraction, an alarm was generated and nearby hospital services were informed so that they could remain ready for any mishaps. They developed various features for face orientation, arm position, facial expression, and eye behavior. These features were combined and fed to the feed-forward neural network (FFNN). Their approach achieved a classification accuracy of 95.62. Along similar lines, in 2019, [PERSON] et al. proposed a deep neural network for the detection of distracted driving [27]. The system also worked in night mode using a near-infrared camera and achieved 92.24% accuracy. In daylight, the accuracy improved to 95.98%. In 2017, [PERSON] et al. presented an architecture to investigate the solutions for distracted driving using performance indicators from on-board kinematic readings [28]. They developed a non-linear autoregressive exogenous (NARX) driving model to predict vehicle speed using distance headway and speed history.