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![Fig. 1. Illustration of Yerkes-Dodson law [1].](profile/Stojan-Trajanovski/publication/320592240/figure/fig1/AS:552985562447872@1508853440562/llustration-of-Yerkes-Dodson-law-1_Q320.jpg)
Driving is an activity that requires considerable alertness. Insufficient attention, imperfect perception, inadequate information processing, and sub-optimal arousal are possible causes of poor human performance. Understanding of these causes and the implementation of effective remedies is of key importance to increase traffic safety and improve dr...
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Background: Challenging Behaviour (CB) occurs in up to 80% of nursing home residents
with dementia. Identifying stressors causing CB is difficult (as residents themselves cannot
indicate what is experienced as stressful), thereby hampering the psychosocial approach.
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Objective
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Citations
... In recent times, wearable sensors have drawn a lot of attention due to their potential to improve virtual reality (VR) driving simulators [6]. Heart rate, galvanic skin response 1. ...
Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.
... Neural networks have become a popular classifier choice for interval methods, due to highly accurate frameworks such as convolutional or recurrent neural networks [40]. The success of a neural net is partly due to its ability to handle unequal time series lengths and optimize model parameters over time [41]. ...
Objective
To evaluate a personalized adaptive training program designed for stress prevention using graduated stress exposure.
Background
Astronauts in the high-risk space mission environment are prone to performance-impairing stress responses, making preemptive stress inoculation essential for their training.
Methods
This work developed an adaptive virtual reality-based system that adjusts environmental stressors based on real-time stress indicators to optimize training stress levels. Sixty-five healthy subjects underwent task training in one of three groups: skill-only (no stressors), fixed-graduated (prescheduled stressor changes), and adaptive. Psychological (subjective stress, task engagement, distress, worry, anxiety, and workload) and physiological (heart rate, heart rate variability, blood pressure, and electrodermal activity) responses were measured.
Results
The adaptive condition showed a significant decrease in heart rate and a decreasing trend in heart rate variability ratio, with no changes in the other training conditions. Distress showed a decreasing trend for the graduated and adaptive conditions. Task engagement showed a significant increase for adaptive and a significant decrease for the graduated condition. All training conditions showed a significant decrease in worry and anxiety and a significant increase in the other heart rate variability metrics.
Conclusion
Although all training conditions mitigated some stress, the preponderance of trial effects for the adaptive condition supports that it is more successful at decreasing stress.
Application
The integration of real-time personalized stress exposure within a VR-based training program not only prepares individuals for high-stress situations by preemptively mitigating stress but also customizes stressor levels to the crew member’s current state, potentially enhancing resilience to future stressors.
... Stacked Denoising Auto-Encoder (SDAE) is a different DL technique that has always just been applied for reducing dimension, which is nonlinear in nature. The architecture of the DAE approach is shown in Figure 6, according to [53]. RNNs are helpful for processing data streams [53]. ...
... The architecture of the DAE approach is shown in Figure 6, according to [53]. RNNs are helpful for processing data streams [53]. The value of every output depends on the preceding iterations, and they are made up of a single network that completes the identical task for each sequence element. ...
... Using data from the input space, a DBM is a stochastic model that is generative and adopts a posterior probability [52,53]. confined to the requirement that its neurons form a bipartite graph; Boltzmann machines are known as DBMs. ...
In this paper, artificial intelligence (AI) and the ideas of machine learning (ML) and deep learning (DL) are introduced gradually. Applying ML techniques like deep neural network (DNN) models has grown in popularity in recent years due to the complexity of healthcare data, which has been increasing. To extract hidden patterns and some other crucial information from the enormous amount of health data, which traditional analytics are unable to locate in a fair amount of time, ML approaches offer cost-effective and productive models for data analysis. We are encouraged to pursue this work because of the quick advancements made in DL approaches. The idea of DL is developing from its theoretical foundations to its applications. Modern ML models that are widely utilized in academia and industry, mostly in image classification and natural language processing, including DNN. Medical imaging technologies, medical healthcare data processing, medical disease diagnostics, and general healthcare all stand to greatly benefit from these developments. We have two goals: first, to conduct a survey on DL techniques for medical pictures, and second, to develop DL-based approaches for image classification. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical image classification; for this, we perform a systematic literature review. A review of various existing techniques in terms of medical image classification indicates some shortcomings that have an impact on the performance of the whole model. This study aims to explore the existing DL approaches, challenges, brief comparisons, and applicability of different medical image processing are also studied and presented. The adoption of fewer datasets, poor use of temporal information, and reduced classification accuracy all contribute to the lower performance model, which is addressed. The study provides a clear explanation of contemporary developments, cutting-edge learning tools, and platforms for DL techniques.
... Because CAVs can move from one access point to another with relative ease, communications are frequently lost and rerouted. Consistent and reliable communication is crucial to the safety of networkassisted AD, but it is challenging to ensure its availability [12][13][14]. ...
... Based on our findings, we suggest a new pipeline that utilizes a hybrid of lidar and camera data to detect objects that were previously invisible. Specifically, we use the language employed by Breitenstein et al. [14] to characterize corner cases at the object level-"instances that have not been observed before"-to frame our concept of anomalies. Since pedestrians and distant objects are of little concern to motorists, we will focus on those by the side of the road. ...
Connected autonomous vehicles can effectively overcome the perceived limitations of human drivers through the use of communication and artificial intelligence technology. However, due to the high dynamics of the vehicular network and the various disruptions and handovers that occur, providing solid communication lines between cars is still difficult, and this might have disastrous consequences. This work proposes a technique for intelligently grouping vehicles in the heterogeneous Cognitive Internet of Vehicles based on their driving behaviours (CIoVs). The driving mode with numerous feature parameters is analysed in the proposed method to precisely capture driving traits. With the goal of facilitating trustworthy clustering of networked autonomous cars, a method based on neural network pattern recognition and the principles of evolutionary algorithms is developed. The cognitive engines can identify the different driving styles and cluster cars that share that style together. We also study the clustering mechanism's communication performance and construct the stability and life duration of clusters. Data from simulations shows that compared to state-of-the-art methods, the suggested mechanism increases reliable communication throughput by around 14.4% and average cluster lifespan by about 11.5%.
... The automobile industry as well as researchers have developed diverse protocols for sensing and detecting human states while driving. Predominantly, experimental data are collected by recruiting subjects to drive, either in the real-world through some pre-defined routes [10], or through a driving simulator [11], where the driving settings can be configured. In most cases, both vehicle status and the driver's physiological signals are of interest. ...
... Yerkes and Dodson Law[21]. ...
Objectives: The overall approach towards Remotely Piloted Aerial System integration into a non-segregated airspace is that the unmanned vehicles should be able to fit into the current air traffic management system, thus meeting all the technical and regulatory requirements to be treated similar to any other airspace user. Such a requirement implies that unmanned aircraft operations should behave as close as possible to manned aviation or at least generate the most negligible possible negative impact on the system. From the air traffic management point of view, this implies that air traffic controllers should be capable of effectively handling different types of RPAS operating in a nominal state but also when suffering a potential contingency. This paper aims to analyse how air traffic controllers involved in managing unmanned aircraft integration into non-segregated airspace are impacted when an unmanned vehicle suffers a contingency. Participants: Six air traffic controllers were the test subjects, complemented by one RPAS pilot and several pseudo-pilots controlling the simulated manned traffic. The project collected real-time simulation data to develop specific indicators to determine how the controllers’ workload increases while managing complex traffic scenarios, including a single RPAS. Study Method: We conducted exhaustive traffic flight simulations, recreating complex airspace scenarios, including various RPAS types and mission-oriented trajectories. The involved RPAS were subjected to two of the most relevant contingencies: loss of the command-and-control link and engine failure. The experiments were evaluated in different operational scenarios, including using autonomous communication technologies to help air traffic controllers track the RPAS operation. Findings: The results indicate that the air traffic controller’s perception and workload are not affected beyond reason by the introduction of an unmanned aircraft as a new element into the non-segregated airspace, even when that aircraft suffers a contingency. The flight-intent technology increases situational awareness, leading to more efficient and safe airspace management. Additional simulations may need to be performed to evaluate the impact on airspace capacity, safety, and workload when various unmanned vehicles are simultaneously inserted.
... Neural networks have become a popular classifier choice for interval methods, due to highly accurate frameworks such as convolutional or recurrent neural networks [40]. The success of a neural net is partly due to its ability to handle unequal time series lengths and optimize model parameters over time [41]. ...
When training for hazardous operations, real-time stress detection is an asset for optimizing task performance and reducing stress. Stress detection systems train a machine-learning model with physiological signals to classify stress levels of unseen data. Unfortunately, individual differences and the time-series nature of physiological signals limit the effectiveness of generalized models and hinder both post-hoc stress detection and real-time monitoring. This study evaluated a personalized stress detection system that selects a personalized subset of features for model training. The system was evaluated post-hoc for real-time deployment. Further, traditional classifiers were assessed for error caused by indirect approximations against a benchmark, optimal probability classifier (Approximate Bayes; ABayes). Healthy participants completed a task with three levels of stressors (low, medium, high), either a complex task in virtual reality (responding to spaceflight emergency fires, n=27) or a simple laboratory-based task (N-back, n=14). Heart rate, blood pressure, electrodermal activity, and respiration were assessed. Personalized features and different interval window sizes were compared. Classification performance was compared for ABayes, support vector machine, decision tree, and random forest. The results demonstrate that a personalized model with time series intervals can classify three stress levels with high accuracy better than a generalized model. However, cross-validation and holdout performance varied for traditional classifiers vs. ABayes, suggesting error from indirect approximations. The selected features changed with windows size and differed between tasks, but blood pressure contributed to the most prominent features. The capability to account for individual difference is a prominent advantage of personalized models, and will likely have a growing presence in future detection systems.
... The automobile industry as well as researchers have developed diverse protocols for sensing and detecting human states while driving. In most cases, experimental data are collected by recruiting subjects to drive, either in the real-world through some pre-defined routes [10], or through a driving simulator [11], where the driving settings can be configured. In most cases, both vehicle status and the driver's physiological signals are of interest. ...
Detecting unsafe driving states, such as stress, drowsiness, and fatigue, is an important component of ensuring driving safety and an essential prerequisite for automatic intervention systems in vehicles. These concerning conditions are primarily connected to the driver's low or high arousal levels. In this study, we describe a framework for processing multimodal physiological time-series from wearable sensors during driving and locating points of prominent change in drivers' physiological arousal state. These points of change could potentially indicate events that require just-in-time intervention. We apply time-series segmentation on heart rate and breathing rate measurements and quantify their robustness in capturing change points in electrodermal activity, treated as a reference index for arousal, as well as on self-reported stress ratings, using three public datasets. Our experiments demonstrate that physiological measures are veritable indicators of change points of arousal and perform robustly across an extensive ablation study.
... In this regard, it has been proven that a high level of stress or cognitive load due to excessive workload can increase the level of fatigue, decrease the subject's working capability, and consequently, bring physical and mental illness, which can lead to workplace absence [9]. Similarly, automatic stress recognition systems can be used in the context of vehicle driving for the detection of excessive mental fatigue states, which can reduce a person's driving skills [10]. Finally, algorithms of stress detection can also be used in recreational areas for the development of systems able to modify their parameters based on the user's emotional state. ...
... The first two rows initialize the counter variable "i" to 0 and set the SND resting state heartbeat frequency (FSNB) to the value arbitrarily chosen. In Rows [3][4][5][6][7][8][9][10][11], the resting state heartbeat of subject "s" is computed. Using this value, the subject-based resampling frequency "fsnc" is evaluated in Row 12. Finally, in Rows [13][14][15], all the signals collected from subject "s" are resampled using the new sampling frequency "fsnc". ...
Physiological responses are currently widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity was considered in the case of Photoplethysmography (PPG), which was successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is herein proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject’s heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization was evaluated in comparison to other normalization procedures in a binary classification task, where cognitive load and relaxed state were considered. The results obtained on two different datasets available in the literature confirmed that applying the proposed normalization strategy permitted increasing the classification performance.
... In this regard, it is indeed proved that a high level of stress or cognitive load due to excessive workload can increase the level of fatigue, decrease the subject's working capability and, consequently, bring physical and mental illness that can lead to absence from the workplace [6]. Similarly, automatic stress recognition systems can be used in the context of vehicle driving for the detection of excessive mental fatigue states that can reduce a person's driving skills [7]. Finally, algorithms of stress detection can be also used in recreational areas for the development of systems able to modify their parameters based on the user's emotional state. ...
Physiological responses are nowadays widely used to recognize the affective state of subjects in real-life scenarios. However, these data are intrinsically subject-dependent, making machine learning techniques for data classification not easily applicable due to inter-subject variability. In this work, the reduction of inter-subject heterogeneity is considered in the case of PhotoPlethysmoGraphy (PPG), which is successfully used to detect stress and evaluate experienced cognitive load. To face the inter-subject heterogeneity, a novel personalized PPG normalization is here proposed. A subject-normalized discrete domain where the PPG signals are properly re-scaled is introduced, considering the subject's heartbeat frequency in resting state conditions. The effectiveness of the proposed normalization is evaluated in comparison with other normalization procedures in a binary classification task, where cognitive load and relaxing state are considered. The results obtained on two different datasets available in the literature confirm that applying the proposed normalization strategy permits to increase classification performance.
