Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.

Drowsiness-related car accidents continue to have a significant effect on road safety. Many of these accidents can be eliminated by alerting the drivers once they start feeling drowsy. This work presents a non-invasive system for real-time driver drowsiness detection using visual features. These features are extracted from videos obtained from a camera installed on the dashboard. The proposed system uses facial landmarks and face mesh detectors to locate the regions of interest where mouth aspect ratio, eye aspect ratio, and head pose features are extracted and fed to three different classifiers: random forest, sequential neural network, and linear support vector machine classifiers. Evaluations of the proposed system over the National Tsing Hua University driver drowsiness detection dataset showed that it can successfully detect and alarm drowsy drivers with an accuracy up to 99%.

With the growth in the wearable device market, wearable sensor-based human activity recognition systems have been gaining increasing interest in research because of their rising demands in many areas. This research presents a novel sensor-based human activity recognition system that utilizes a unique feature extraction technique associated with a deep learning method for classification. One of the main contributions of this work is dividing the sensor sequences time-wise into non-overlapping 2D segments. Then, statistical features are computed from each 2D segment using two approaches; the first approach computes features from the raw sensor readings, while the second approach applies time-series differencing to sensor readings prior to feature calculations. Applying time-series differencing to 2D segments helps in identifying the underlying structure and dynamics of the sensor reading across time. This work experiments with different numbers of 2D segments of sensor reading sequences. Also, it reports results with and without the use of different components of the proposed system. Additionally, it analyses the best-performing models’ complexity, comparing them with other models trained by integrating the proposed method with an existing transformer network. All of these arrangements are tested with different deep-learning architectures supported by an attention layer to enhance the model. Four benchmark datasets are used to perform several experiments, namely, mHealth, USC-HAD, UCI-HAR, and DSA. The experimental results revealed that the proposed system outperforms human activity recognition rates reported in the most recent studies. Specifically, this work reports recognition rates of 99.17%, 81.07%, 99.44%, and 94.03% for the four datasets, respectively.

Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist’s judgment. Moreover, detecting the tortuosity severity of the retinal vessels’, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.

The recent development in the wearable devices domain raised interest in wearable sensor-based human activity recognition systems research. This paper introduces a novel human activity recognition system that uses a unique feature extraction method combined with a deep learning network associated with an attention layer. One of the main contributions of this work is the time-wise division of sensor sequences into non-overlapping segments. Each segment is treated as a 2D image, and consequently, we apply image-based feature extraction techniques to it. This includes the use of two-dimensional discrete wavelet transformation to extract useful features from the sensor readings. Experimental results showed that the system outperforms the recognition rates and f1-scores reported in most recent studies in the literature. Specifically, we report recognition rates of 99.44% for the UCI-HAR dataset, respectively.