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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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Objective
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Citations
... 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.
... 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]. ...
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... 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.
... We subdivided the two-dimensional emotion model for arousal-valence discussed in Section 2. Most emotion recognition studies use Russell's model, i.e., binary classification into high and low. The criteria vary slightly from case to case, using either reference points obtained through self-assessment or commonly used reference values [26]. However, other studies showed that arousal-valence data were evenly distributed on the two-dimensional space [27,28] and arousal and valence could be divided into several stages depending on the user's feelings. ...
With the advancement of human-computer interaction and artificial intelligence, emotion recognition has received significant research attention. The most commonly used technique for emotion recognition is EEG, which is directly associated with the central nervous system and contains strong emotional features. However, there are some disadvantages to using EEG signals. They require high dimensionality, diverse and complex processing procedures which make real-time computation difficult. In addition, there are problems in data acquisition and interpretation due to body movement or reduced concentration of the experimenter. In this paper, we used photoplethysmography (PPG) and electromyography (EMG) to record signals. Firstly, we segmented the emotion data into 10-pulses during preprocessing to identify emotions with short period signals. These segmented data were input to the proposed bimodal stacked sparse
auto-encoder model. To enhance recognition performance, we adopted a bimodal structure to extract shared PPG and EMG representations. This approach provided more detailed arousal-valence mapping compared with the current high/low binary classification. We created a dataset of PPG and EMG signals, called the emotion dataset dividing into four classes to help understand emotion levels. We achieved high performance of 80.18% and 75.86% for arousal and valence, respectively, despite more class classification. Experimental results validated that the proposed method significantly enhanced emotion recognition performance.
... Some microarousal detection systems have been installed in mobile phones, using wearable devices, such as combined equipment of a smart watch and a heart rate belt [103], and wrist-worn devices [104] for real-time monitoring. ...
... A seven-layer CNN was used to detect arousal levels, namely, under-aroused, normal and over aroused levels of professional truck drivers [104]. Raw physiological signals, such as heart rate, skin conductance, and skin temperature, were collected from 11 participants via wrist-worn devices. ...
... Second, in some studies, the more specific arousal labels were discussed with data sets. For example, Saeed et al. [104] adopted a database that contained two types of arousals, i.e., the under-arousal and over-arousal. In further studies, the algorithm for the classification of sleep arousals needs to be upgraded, with different types of arousal, such as respiratory, movement, and spontaneous arousal. ...
Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.
... In most of these applications, a fundamental role is played by the ability of the machines to evaluate the emotional state of human users. For example, in context of vehicle driving, the automotive system can be used to monitor the mental and physical state of the driver in order to detect possible situations of excessive stress or fatigue and, thus, keep the driver alert [20,22]. Other HRI applications concern their use in the working environment. ...
ABSTRACT
Physiological data are nowadays frequently used to recognize the
affective state of subjects while performing different tasks. Automatic recognition of a stressful state as a consequence of a high level of cognitive load is significant to prevent illnesses like depression, anxiety and sleep disorders that are often due to excessive workload.
The spread of wearable sensors that are increasingly reliable and
comfortable makes them easy to use in day-life activities. However,
due to the nature of experiments that involve subjects, the cardinality of the acquired data is often low, making difficult to train
deep learning methods from the scratch. In this paper we consider
the photopletismography (PPG) that measures the blood volume
registered just under the skin, which can be used to obtain the heart
rate of the subject. It is well known that PPG data are particularly
relevant to detect high level of arousal that is activated by stress.
We show that, converting monodimensional photopletismography
(PPG) data into bi-dimensional signals it is possible to apply a pre-trained CNN, obtaining deep features that outperform handcrafted ones in classification tasks, especially introducing feature selections
strategies to avoid curse of dimensionality.
