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Nowadays, the performance of classification methods used in physiological signals for healthcare purposes governs computer-based analysis. A prevalent research area in biosignal analysis with the aim of stress recognition and classification is to design algorithms that outperform established approaches. Accurate stress realization in drivers would address significant costs of driving-induced stress and increase drivers’ safety if unobstructed and fully automated stress detection devices developed. This procedure faces some challenges such as human stress detection and recognition, along with simulating stress in computing devices. For stress computing, the initial step is to detect stress which requires capturing human state data. The next step is to extract the most relevant features to find meaningful patterns to analyze data which could be performed by mathematical analysis and machine learning tools. In this paper, a review of the recent advancement in signal processing, feature extraction, and machine learning methods with a focus on galvanic skin response (GSR) analysis is presented. This review enlightens the reader with common methodologies, issues, and future opportunities in this research area.

The computerized world has made individual depleted with PC stress which makes issue in day by day life. Stress causes medical problems like coronary illness, migraines, tension, melancholy, apprehensive harm and so on. We speak to stress recognition framework by applying methods of ML on some bio-signal information of individual. Stress is characterized as the difficulty in mental harmony. In setting of a clinical field, stress is mental factor that causes genuine or mental strain.Stress identification is a developing zone. Stress location can forestall numerous issues like cardiovascular variations from the norm identified with rythm or arrhythmia. The accessible natural signs are ECG, EMG, Respiration, GSR and so on and these signs changes when stress is instigated. Henceforth these signs can be utilized to recognize feelings of anxiety. As there are distinctive ECG extraction procedures are accessible, we use ECG signals for Biological sign examination. EDR(ECG determined Respiration) can be extricated from ECG. EDR is respiratory sign data. We can likewise infer RR span, QT stretch from ECG required being developed of the model.There are distinctive ML strategies for stress location. In this study we will talk about those strategies.

The report presents the study of recognition of stress states through Multilayer Backpropagation Neural Networks (MBNN) based on extracted sound indicators at speech signal analysis. Experimental neural architectures with tangent-sigmoid and logarithmic-sigmoid functions in the two hidden layers with different initial activation were tested. The models with initial logarithmic-sigmoid type, where the obtained maximum accuracy is 75.00%, are defined as inapplicable. In MBNNs with linear and tangential-sigmoidal functions in the initial layers, significantly better adequacy was found as the highest levels of the indicator reached 100.00%.

A noise cancellation and MTEO removal filtering for extracting of basic elements of ECG signal is applied in the research, presented in this paper. The processing is performed on ECG signals from CLAS: A Database for Cognitive Load, Affect and Stress Recognition. The algorithms used are implemented in Matlab, the extraction of elements from the ECG signal has been successfully performed.

emotional states detection from physiological signals has many potential applications. In Human-Machine or Human-Human interaction systems, stress detection could provide users with improved services and can be a tool for monitoring and preventing potential stress-related pathologies. Traditional machine learning techniques for automatic stress recognition have been used in previous researches but they sometimes present specific limitations. The emergence of deep learning permits the reveal of underlying patterns in body response witch, otherwise would not be easily observed. In this paper we explore the application of Long Short-Term Memory (LSTM) and Deep Neural (DNN) Networks for real time stress monitoring in young gamers. We base our study on their body responses. For this, we use physiological signals such as the electrocardiography (ECG), the electrodermal activity (EDA), and the electromyography (EMG), measured by non-invasive wearable sensors. The result of the study provides an evaluation of both models’ capacity in predicting real time gamers’ emotional state built on the variation of their physiological parameters.

In this paper, we propose a stress recognition algorithm using face images and face landmarks. In the case of stress recognition using a biological signal or thermal image, which is being studied a lot, a device for acquiring the corresponding information is required. In order to remedy this shortcoming, we proposed an algorithm that can recognize stress from images acquired with a general camera. We also designed a deep neural network that receives facial landmarks as input to take advantage of the fact that eye, mouth, and head movements are different from normal situations when a person is stressed. Experimental results show that the proposed algorithm recognizes stress more effectively.

Stress emotion recognition from speech utterances seeks a humongous attention among the researchers. Stress is one of the serious problems in the current society. Due to this people in different sectors are suffering from many severe health issues which may lead to huge economic damage. In this paper a detailed survey has been made on the recognition of stress emotion of speech samples which addresses two important areas. The primary one deals with the suitable databases which are used for stress emotion recognition. The later one deals with the choice of best features and also the classifiers along with their performances used for stress emotion recognition. Conclusion deals with the performance evaluations and limitations of the stress emotion recognition system.

In this study, we investigate reliable heart rate variability (HRV) parameters in order to recognize stress. An experiment protocol was established including different stressors which correspond to a range of everyday life conditions. A personalized baseline was formulated for each participant in order to eliminate inter-subject variability and to normalize data providing a common reference for the whole dataset. The extracted HRV features were transformed accordingly using the pairwise transformation in order to take into account the personalized baseline of each phase in constructing the stress model. The most robust features were selected using the minimum Redundancy Maximum Relevance (mRMR) selection algorithm. The selected features fed machine learning systems achieving a classification accuracy of 84.4% using 10-fold cross-validation.

This paper presents a novel approach to monitor office workers' behavioral patterns and heart rate variability. We integrated an EMFi sensor into a chair to measure the pressure changes caused by a user's body movements and heartbeat. Then, we employed machine learning methods to develop a classification model through which different work behaviors (body moving, typing, talking and browsing) could be recognized from the sensor data. Subsequently, we developed a BCG processing method to process the data recognized as 'browsing' and further calculate heart rate variability. The results show that the developed model achieved classification accuracies of up to 91% and the HRV could be calculated effectively with an average error of 5.77ms. By combining these behavioral and physiological measures, the proposed approach portrays work-related stress in a more comprehensive manner and could contribute an unobtrusive early stress detection system for future smart offices.

This paper used small-scale analysis with single modality ECG signal to extract five critical features to train an effective 3 classification stress recognition model through the datasets of Stress Recognition in Automatic Drive. Using the local Hurst exponents and small-scale analysis, we could achieve 98.39% accuracy rate in detecting the three classes of stress.

This paper has proposed a system that can be analyzed of speech stress recognition. The proposed method (ensemble SVM and NN) is analyzed comparatively proven to have high accuracy. The ensemble method has been applied to improve machine learning ability in identifying with a small number of datasets. It is caused; due to stress is one of the unconscious emotions. Stress can be recognized by speech however it is not robust. It was caused by small datasets. In this work, we use the sample of SUSAS dataset. The dataset is divided into 10 groups by the combinational method. Each group of data trained using SVM then combined it with trained NN. The experimental results show that with a small dataset, the proposed method outperformed the previous methods.

This paper investigates variations in head pose features in response to specific stressors. A proper experiment consisting of neutral and stressful states was performed aiming to cover different types of stress affect. Then, features related to head movements and pose were computationally estimated and analyzed. Towards this direction, facial landmarks were fitted using Active Appearance Models (AAM). Using the 2D AAM facial landmarks, a 3D head pose model was estimated revealing head inclinations. Results indicate that specific stress conditions increase head mobility and mobility velocity, in both translational and rotational features. Even though stress modulates head movements and velocities, the most prominent increases are presented during tasks that include participant's speech. The degree and the intensity of the interaction effect between speech and stress should be investigated in more detail. The analysis reports that specific head pose features can be significant stress indicators that could contribute among other facial cues in reliable stress recognition.

Stress become a routine and part of human life. It can keep the person enthusiastic and energetic. However, unmanageable stress could be deathly. The main consideration of this paper is to represent a new ASIC design for stress recognition algorithm based on detection of the stress signs. In fact, plenty of information could be extracted from the heart signal for multiple illnesses which make it reliable and more convenience to be used [3]. Thus, in this framework, Heart Rate Variability (HRV) is used in distinguishing among the stress levels such as low stress, moderate stress, and high stress. Two stage SVM classifiers were used and it achieved 96.4% accuracy in the first stage and 80.5% in the second stage. The system consumed a total area of 0.073 mm2 and a consumed power of 33.4845uW.

Mental stress is a critical problem in our modern society. This form of stress strongly affects our well being, and technology is needed to help us to manage health problems. The ability to automatically recognize a person's mental stress can be fundamental in supporting stress and health management. This research focuses on the use of mobile thermal imaging, a new and less explored sensor, to merge the measurement of multiple physiological signatures into one sensor and to build a reliable mental stress automatic recognition model. Mobile thermal imaging has greater potentials for real-world applications given that it is small and light weight, and requires low computation cost. To make mobile thermal imaging a robust multimodal stress sensor, we have so far contributed: i) a new robust respiration tracking method; and ii) a novel respiration-based automatic stress recognition model that works in ubiquitous settings. We are currently investigating new thermal signatures from underexplored body regions (i.e. trapezius muscle) and formulating a research framework to fuse multiple thermal signatures for more reliable stress recognition outcomes.

Many studies have shown that the majority of maritime accidents/incidents are attributed to human errors as the initiating cause. Efforts have been made to study human factors that can result in a safer maritime transportation. Among all techniques, Electroencephalogram (EEG) has the advantages such as high time resolution, possibility to continuously monitor brain states with high accuracy, recognition of human mental workload, emotion, stress, vigilance, etc. In this paper, we designed and carried out an experiment to collect the EEG signals to study stress and sharing of the mental workload among crew members during collaboration tasks performance on the ship's bridge virtual simulator. Four maritime trainees were monitored in the experiment. Each of them had a role such as an officer on watch, captain, pilot, or steersman. The results show that the captain had the highest stress and workload. However, the other three trainees experienced low workload and stress due to shared work and responsibility. The EEG is a promising evaluation tool to be used in the human factors study in the maritime domain. National Research Foundation (NRF) Singapore Maritime Institute (SMI) Accepted version This research is supported by Singapore Maritime Institute and by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. We would like to acknowledge the final year project students of School of MAE of Nanyang Technological University and personally Lee Jian Wei for his contribution in this work.

In this work, real time stress level recognition from Electroencephalogram (EEG) signal is proposed. Stress is a mental condition that effects the brain electrical activity to be different from the normal state. A system is designed to classify three levels of stress; Low Stress, Moderate Stress and High Stress. Validation of algorithm is carried out using by Stroop color-word test as stimuli to induce various levels of stress. EEG signals are collected using EEG reusable electrodes, which are placed on forehead scalp during the experiment. Measure of stress is taken by means of questionnaires method. Discrete Wavelet Transform (DWT) is used for pre-processing and using Support Vector Machine (SVM) as the classifier. Further analysis of stress is done by using trained algorithm as reference. Signals collected are continually given to algorithm to verify stress, three levels of stress can be recognized. Stress levels of the person under test are seen on screen using graph.

Stress is a response to time pressure or negative environmental conditions. If its stimulus iterates or stays for a long time, it affects health conditions. Thus, stress recognition is an important issue. Traditional systems for this purpose are mostly contact-based, i.e., they require a sensor to be in touch with the body which is not always practical. Contact-free monitoring of the stress by a camera [1, 2] can be an alternative. These systems usually utilize only an RGB or a thermal camera to recognize stress. To the best of our knowledge, the only work on fusion of these two modalities for stress recognition is [3] which uses a feature level fusion of the two modalities. The features in [3] are extracted directly from pixel values. In this paper we show that extracting the features from super-pixels, followed by decision level fusion results in a system outperforming [3]. The experimental results on ANUstressDB database show that our system achieves 89% classification accuracy.

Strong and weak stress were elicited by real-scene thesis defense and pre-defending presentation of the thesis work, and tri-axial accelerometer data were acquired and analyzed for their ability to recognize strong and weak stress. Twenty-six subjects (7 females and 19 males) participated in the data acquisition experiment. A support vector machine classifier obtained a correct rate of 92.31% in the binary classification of the 26 subjects' tri-axial accelerometer data acquired during strong stress and weak stress.

This paper presents the results of a study developing expert system to support stress recognition based on Artificial Neural Network (ANN). Developed ANN is trained using data from Physionet database and collected data from other researchers. The implemented system for stress recognition uses drivers ECG signal, Galvanic Skin Response and Respiration Rate as parameters. Developed neural network was validated with 77 samples. Samples are obtained from subjects using Pasco sensors in 7D cinemas. Out of 77 samples, in 71% of subjects higher level of stress is recognized, while 29% of subjects are classified as subjects with normal vital functions. An accuracy of 99% and specificity of 98% is obtained.

Stress affects people's health and well-being of the world's economies. Despite the progress in physiological stress recognition, there are problems that require solutions in the creation of automated systems of stress determination in prolonged real-life situations. These tasks are analysis of stress in daily life, during physical activity and personalization of this analysis. We described these tasks and answered the question: "Why it is important to use context and behavioral data into stress recognition systems?" We have presented sensors classification included behavioral and context data, physiological and physical features and others, like self-reports and questionnaires. And we have performed the stress model.

Stress is a serious concern facing our world today, motivating the development of better objective understanding using non-intrusive means for stress recognition. The aim for the work was to use thermal imaging of facial regions to detect stress automatically. The work uses facial regions captured in videos in thermal (TS) and visible (VS) spectrums and introduces our database ANU StressDB. It describes the experiment conducted for acquiring TS and VS videos of observers of stressed and not-stressed films for the ANU StressDB. Further, it presents an application of local binary patterns on three orthogonal planes (LBP-TOP) on VS and TS videos for stress recognition. It proposes a novel method to capture dynamic thermal patterns in histograms (HDTP) to utilize thermal and spatio-temporal characteristics associated in TS videos. Individual-independent support vector machine classifiers were developed for stress recognition. Results show that a fusion of facial patterns from VS and TS videos produced significantly better stress recognition rates than patterns from only VS or TS videos with p

Stress is a major problem in our world today motivating objective understanding of how average individuals respond to stress in a typical activities. The main aim for this paper is to determine whether stress can be recognized using individual-independent computational models from sensor based stress response signals induced by films with typical stressful content. Another aim is to determine whether a consumer electroencephalogram (EEG) sensor device, which is portable, less obtrusive and relatively inexpensive, can be used for stress recognition. A support vector machine and an artificial neural network based models were developed to recognize stress using various physiological and physical signals. The models produced stress classification with 95% accuracy. Using the data obtained from the consumer device, the models produced stress classification with 91% accuracy. Statistical analysis of the results showed that the classification results from the physiological and physical signals are not statistically different to the results from the consumer device implying that the consumer device can be used for recognizing stress in typical virtual environments.

Recently there is a great interest in artificial systems able to understand and recognize human emotions. In this paper an Emotion Recognition System based on classical neural networks and neuro-fuzzy classifiers is proposed. Emotion recognition is performed in real time starting from a video stream acquired by a common webcam monitoring the user's face. Neurofuzzy classifiers, in comparison with Multi Layer Perceptron trained by EBP algorithm, show very short training times, allowing applications with easy and automated set up procedures, to be used in a wide range of applications, from entertainment to safety. The algorithm yields very interesting performances and can be adopted to recognize emotions as well as possible pathological conditions of the individual to be monitored.