Identifying accident patterns is one of the most vital research foci of driving analysis. Environmental or safety applications and the growing area of fleet management all benefit from accident detection contributions by minimizing the risk vehicles and drivers are subject to, improving their service and reducing overhead costs. Some solutions have been proposed in the past literature for automated accident detection that are mainly based on traffic data or external sensors. However, traffic data can be difficult to access, while external sensors can end up being difficult to set up and unreliable, depending on how they are used. Additionally, the scarcity of accident detection data has limited the type of approaches used in the past, leaving in particular, machine learning (ML) relatively unexplored. Thus, in this paper, we propose a ML framework for automated car accident detection based on mutimodal in-car sensors. Our work is a unique and innovative study on detecting real-world driving accidents by applying state-of-the-art feature extraction methods using basic sensors in cars. In total, five different feature extraction approaches, including techniques based on feature engineering and feature learning with deep learning are evaluated on the strategic highway research program (SHRP2) naturalistic driving study (NDS) crash data set. The main observations of this study are as follows: (1) CNN features with a SVM classifier obtain very promising results, outperforming all other tested approaches. (2) Feature engineering and feature learning approaches were finding different best performing features. Therefore, our fusion experiment indicates that these two feature sets can be efficiently combined. (3) Unsupervised feature extraction remarkably achieves a notable performance score.

In the evolving field of human–computer interaction (HCI), gesture recognition has emerged as a critical focus, with smart gloves equipped with sensors playing one of the most important roles. Despite the significance of dynamic gesture recognition, most research on data gloves has concentrated on static gestures, with only a small percentage addressing dynamic gestures or both. This study explores the development of a low-cost smart glove prototype designed to capture and classify dynamic hand gestures for game control and presents a prototype of data gloves equipped with five flex sensors, five force sensors, and one inertial measurement unit (IMU) sensor. To classify dynamic gestures, we developed a neural network-based classifier, utilizing a convolutional neural network (CNN) with three two-dimensional convolutional layers and rectified linear unit (ReLU) activation where its accuracy was 90%. The developed glove effectively captures dynamic gestures for game control, achieving high classification accuracy, precision, and recall, as evidenced by the confusion matrix and training metrics. Despite limitations in the number of gestures and participants, the solution offers a cost-effective and accurate approach to gesture recognition, with potential applications in VR/AR environments.

Background and objectives: Stress is a common term used to describe various adverse psychological conditions. Students in the dentistry field face many negative psychological outcomes. The core factors for stress among dental students are related to their training course and social contacts with peers. This research aimed to assess the stress of dental students depending on their gender and study year. Materials and methods: We used the Perceived Stress Scale (PSS-10) and Mini-COPE questionnaire. The surveys were conducted among 446 dental students (320 women and 126 men) at the Faculty of Medical Sciences of the Medical University of Silesia in Katowice. Results: For the second-year and fifth-year students, the differences in scores were statistically significant, while in both cases, men had significantly lower values on the analysed scale. The results of the Kruskal-Wallis test indicated significantly lower values on the PSS-10 scale for the third-year and fourth-year students than in first-year students. The performed statistical analysis of the data obtained from the Mini-COPE questionnaire showed significant differences between men and women in individual years of study. In the first year, women chose more often the strategies related to turning to religion (p = 0.007), seeking emotional support (p = 0.046), seeking instrumental support (p = 0.045) and dealing with something else (p = 0.029) in coping with stress than men. Conclusions: The highest level of stress was found among first-year dental students. Moreover, women were characterised with higher stress levels than men. Men more often use psychoactive substances and resort to a sense of humour to cope with stress. On the other hand, women turn to religion, seek instrumental and emotional support.

Introduction and Objective: Dentists experience high amounts of professional stress beginning with their student years in dental school. This stress, given its early onset, may negatively impact the personal and professional lives of these individuals, as well as the quality of their clinical work. We sought to create an objective scale to evaluate the levels of stress in students at different stages of their education, as well as in practicing physicians.Materials and Methods: Thirty dental students participated in this study, with 10 students each selected from junior, mid-senior, and senior classes. They were randomly divided into two groups in which one group was subjected to stressors while the other group was not. JINS MEME ES_R (JINS) smart glasses and Garmin Vivoactive 3 smartwatches were used to obtain data, including electrooculography (EOG), heart rate (HR), and accelerometer (ACC) and gyroscope (GYRO) feedback, while the subjects performed a dental exercise on a phantom tooth.Results: The heart rates of more experienced students were lower than those of the junior students. The EOG, ACC, and GYRO signals showed multiple differences in the measurement of amplitudes and frequency of episodes.Conclusion: Our pilot results show that electronic tools, like smart glasses with software and sensors, are useful for monitoring the stress levels of dental students in preclinical operating conditions. We would like to further assess the stress levels in students performing dental procedures on phantom teeth and in later clinical interactions with patients.

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.

Background: Dental schools are considered to be a very stressful environment; the stress levels of dental students are higher than those of the general population. The aim of this study was to assess the level of stress among dental students while performing specific dental procedures. Methods: A survey was conducted among 257 participants. We used an original questionnaire, which consisted of 14 questions assigned to three categories: I—Diagnosis, II—Caries Treatment, and III—Endodontic Treatment. Each participant marked their perceived level of stress during the performed dental treatment procedures. The scale included values of 0–6, where 0 indicates no stress, while 6 indicates high stress. Results: Third- ($p=0.006$) and fourth-year ($p=0.009$) women were characterized by a higher level of perceived stress during dental procedures related to caries treatment. Caries treatment procedures were the most stressful for 18.3% of third-year students, 4.3% of fourth-year students, and 3.2% of fifth-year students. Furthermore, 63.4% of third-year students, 47.3% of fourth-year students, and 17.2% of fifth-year students indicated that they felt a high level of stress when performing endodontic procedures. Conclusion: Third- and fourth-year female students are characterized by a higher level of stress during caries and endodontic treatment procedures. The most stressful treatments for participants were endodontic treatment procedures.