Search

Wearable, sensor-equipped devices { often referred to as wearables -- became increasingly available and ubiquitous in recent years. The rising social acceptance of body-worn technology is a driver for the increasing adoption of wearables on the consumer level. Especially, the health and fitness sector is popular, since wearables can offer continuous sensing of various aspects of our lives, like e.g. physical activity or heart rate. In our modern, hectic lives, stress becomes an increasing factor diminishing our health. Chronic stress is highly related health issues and a shortening of lifespan [3]. Successful, ubiquitous sensing technologies could support the detection and prevention of stress in everyday life.

The electroencephalogram (EEG) is a device for measuring the electrical activity of the brain; it has the ability to detect the waves at various frequencies. The device uses a small electrode to record the measurements. The EEG waves can be used to detect many activities in the brain, such as stress. This study identifies stress using EEG signals. Stress causes a certain range of frequencies in the range to change their activities, in which the changes can be analyzed. Test results were filtered properly, and the frequency bands measured. The data shows the difference in the ratio of beta waves and alpha waves in the brain as a result of stress. The changes in the ratio will be able to show the degree of stress encountered.

Stress is a major problem in our society today and poses major concerns for the future. It is important to gain an objective understanding of how average individuals respond to events they observe in typical environments they encounter. We developed a computational model of stress based on objective human responses collected from human observers of environments. In the process, we investigated whether a computational model can be developed to recognize observer stress in abstract virtual environments text, virtual environments films and real environments real-life settings using physiological and physical response sensor signals. Our work proposes an architecture for a computational observer stress model. The architecture was used it to implement models for the different types of environments. Sensors appropriate to the different types of environment were investigated where the aims were to achieve unobtrusive methods for stress response signal collection, reduce encumbrance and hence, enhance methods to capture natural observer behaviors and produce stress models that recognized stress more robustly. We discuss the motivations for each investigation and detail the experiments we conducted to collect stress data sets for observers of the different types of environments. We describe individual-independent artificial neural network and support vector machine based model classifiers that were developed to recognize stress patterns from observer response signals. The classifiers were extended to include a genetic algorithm which was used to select features that were better for stress recognition and reduce the use of redundant features. The outcomes of this research provide a possible future extension on managing stress objectively.

The detection of stress at early stages is beneficial to both individuals and communities. However, traditional stress detection methods that use physiological signals are contact-based and require sensors to be in contact with test subjects for measurement. In this paper, we present a method to detect psychological stress in a non-contact manner using a human physiological response. In particular, we utilize a hyperspectral imaging (HSI) technique to extract the tissue oxygen saturation (StO2) value as a physiological feature for stress detection. Our experimental results indicate that this new feature may be independent from perspiration and ambient temperature. Trier Social Stress Tests (TSSTs) on 21 volunteers demonstrated a significant difference ( $p\lt 0.005$ ) and a large practical discrimination (d = 1.37) between normalized baseline and stress StO2 levels. The accuracy for stress recognition from baseline using a binary classifier was 76.19 and 88.1 percent for the automatic and manual selections of the classifier threshold, respectively. These results suggest that the StO2 level could serve as a new modality to recognize stress at standoff distances.

Study Objective To measure the safety culture in the robotics surgery operating room before and after implementation of the Robotic Operating Room Computerized Checklist (RORCC). Design Prospective study. Subjects Gynecology surgical staff (n = 32). Setting An urban community hospital. Interventions The Safety Attitudes Questionnaire domains examined were teamwork, safety, job satisfaction, stress recognition, perceptions of management, and working conditions. Questions and domains were described using percent agreement and the Cronbach alpha. Paired t -tests were used to describe differences before and after implementation of the checklist. Measurements and Main Results Mean (SD) staff age was 46.7 (9.5) years, and most were women (78%) and worked full-time (97%). Twenty respondents (83% of nurses, 80% of surgeons, 66% of surgical technicians, and 33% of certified registered nurse anesthetists) completed the Safety Attitudes Questionnaire; 6 were excluded because of non-matching identifiers. Before RORCC implementation, the highest quality of communication and collaboration was reported by surgeons and surgical technicians (100%). Certified registered nurse anesthetists reported only adequate levels of communication and collaboration with other positions. Most staff reported positive responses for teamwork (48%; α = 0.81), safety (47%; α = 0.75), working conditions (37%; α = 0.55), stress recognition (26%; α = 0.71), and perceptions of management (32%; α = 0.52). No differences were observed after RORCC implementation. Conclusion Quality of communication and collaboration in the gynecology robotics operating room is high between most positions; however, safety attitude responses are low overall. No differences after RORCC implementation and low response rates may highlight lack of staff support.

Two hundred and eighty Health care; physicians, nurses and technicians were included in the study, 133 (47.5) from 6th discrete HCF, 54 (19.3%) from Duwaiqa and 93 (33.2%) from Al Haggana. Ages ranged between 22 and 59 years (35.14 ± 10.13), years of experience in the study group ranged between 1 and 35 years (8.72 ± 8.18) and years of work in group ranged between 1 & 40 years (10.43 ± 8.33). Doctors and nurses were males (72.55%) & females (86%) respectively while technicians were mostly males (60%). The teamwork climate score was 3.98 ± 0.87, 64.0% answered high or very high, 16.4% answered inadequate and 18% had answered few or very few. Safety climate score was 3.61 ± 0.63, 49.28% had high or very high score, 17.14% answered inadequate and 32.15% had answered few or very few. Job satisfaction score was 3.91 ± 0.80, 32.15% had answered few or very few, 17.14% answered inadequate and 49.28% answered high or very high. Stress recognition score was 3.61 ± 0.79, 25% had answered few or very few, 28.6% answered inadequate and 45.7% answered high or very high. Perception of management score was 3.48 ± 0.80, 23.2% had answered few or very few, 17.8% answered inadequate and 57.6% answered high or very high. Working condition score was 3.51 ± 0.84, 46.8% had answered few or very few, 17.1% answered inadequate and 35.7% answered high or very high. A significant difference regarding team work score, safety climate score, perception of management score, working condition score with highest value in doctors and lowest in technicians. On the other hand no significant difference was detected regarding job satisfaction score and stress recognition score. A significant difference regarding team work score, safety climate score and perception of management score with high values among older groups. No significant difference was detected regarding job satisfaction score, stress recognition score and working condition score. Also, a significant difference regarding team work score and safety climate score with higher value in males. Significant difference regarding job satisfaction score and working condition score was detected with higher value in females. No significant difference was detected regarding stress recognition score and perception of management scores.

The purpose of this study is to provide an academic basis regarding the necessity of managing depression and stress among metabolic syndrome patients by understanding 5 components of metabolic syndrome, perceived stress, and degree of depression, and by investigating their association using the national nutrition survey reference. This study was conducted by using mental health surveys and health screening test data of the 5th (2010∼2012) primitive data of the national health and nutrition survey. A total of 19,599 respondents over 19 years of age were selected for the final analysis. The level of depression and stress was set as the dependent variable to identify its connection with 5 components of the metabolic syndrome. For the stress recognition, none of the metabolic syndrome components showed a significant correlation. For experiencing the depression symptom, the fasting glucose among the factors showed a significant correlation (p ＜0.05) among the metabolic syndrome factors. When it falls within the criteria of fasting glucose of metabolic syndrome, it has a great probability of falling under the group who experienced greater depression symptoms. As a result of the analysis by controlling cardiovascular and cerebrovascular disease which is tightly related with metabolic syndrome and depression, this study observed that glucose out of 5 metabolic syndrome components is related with depression.

Good mental health is important in our daily life. A person commonly finds stress as a barrier to enhance an individual’s performance. Be reminded that not all people have the same level of stress because different people have dissimilar problems in their life. In addition, different level of age and gender will affect unequal amount of stress. Electrocardiogram (ECG) signal is an electrical indicator of the heart that can detect changes of human response which relates to our emotions and reactions. Thus, this research proposed a non-intrusive detector to identify stress level for both gender and different classification of age using the ECG. A total of 30 healthy subjects were involved during the data acquisition stage. Data acquisition which initialize ECG data were divided into two conditions; which are normal and stress states. ECG data for normal state only need the participant to breath in and out normally. In other hand, the participants also need to undergo Stroop Colour word test as a stress inducer to represent ECG in stress state. Then, Sgolay filter was selected in the pre-processing stage to remove artifacts in the signal. The process was followed by feature extraction of the ECG signal and finally classified using RR Interval (RRI), different amplitudes of R peaks and Cardioid graph were used to evaluate the performance of the proposed technique. As a result, Class 5 (age range between 50-59 years old) marks the highest changes of stress level rather than other classes, while women are more affected by stress rather than men by showing tremendous percentage changes between normal and stress level over the proposed classifiers. The result proves that ECG signals can be used as an alternative mechanism to recognize stress more efficiently with the integration of gender and age variabilities.

Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.

Background: community-based midwifery practice has been promoted in the UK maternity policy over the last decade as a means of increasing continuity of care. However, there have been growing concerns to suggest that the community-based continuity model may not be sustainable due to the high levels of occupational burnout in midwives resulted by increased on-call work. Aims: this paper attempted to identify work factors associated with the levels of burnout in community midwives as compared to hospital midwives, aiming at contributing to the debate of organising sustainable midwifery care. Methods: a statistical analysis was conducted drawing on data from a survey of all midwives working at one Hospital Trust in England (n¼ 238). Occupational burnout was measured using the Maslach Burnout Inventory (MBI). Results: the sample midwives (n¼ 128, 54%) had significantly higher levels of burnout compared to the reference groups. Multiple regression analysis identified as follows: (1) high levels of occupational autonomy were a key protective factor of burnout, and more prevalent in the community, (2) working hours were positively associated with burnout, and community midwives were more likely to have higher levels of stress recognition, and (3) support for work-life-balance from the Trust had a significant protective effect on the levels of burnout. Conclusion: the results should be taken into account in the maternity policy in order to incorporate continuity of care and sustainable organisation of midwifery care.

Nowadays, electroencephalographic (EEG) recordings receive increasing attention in the field of emotions recognition with physiological variables. Moreover, the nonlinear nature of EEG signals suggests that nonlinear techniques could be more suitable than linear methodologies for the assessment of mental processes triggered under different emotions. One of the most relevant states is distress (the negative aspect of stress), because of its enormous influence in developed countries and its countless adverse effects in health. As a result, many researches have shown their interest in distress in the last few years. In the present study, a predictability-based entropy measure called amplitude-aware permutation entropy (AAPE) was applied to discern between calm and distress states. EEG signals from 32 channels were individually assessed to obtain the discriminatory ability of each single electrode. After that, only 2 out of 32 EEG channels were combined in a logistic regression model, reaching a global classification accuracy over 73%.

In modern society, the stress of an individual has been found to be a common problem. Continuous stress can lead to various mental and physical problems and especially for the people who always face emergency situations (e.g., fireman): it may alter their actions and put them in danger. Therefore, it is meaningful to provide the assessment of the stress of an individual. Based on this idea, the Psypocket project is proposed which is aimed at making a portable system able to analyze accurately the stress state of an individual based on his physiological, psychological and behavioural modifications. It should then offer solutions for feedback to regulate this state.The research of this thesis is an essential part of the Psypocket project. In this thesis, we discuss the feasibility and the interest of stress recognition from heterogeneous data. Not only physiological signals, such as Electrocardiography (ECG), Electromyography (EMG) and Electrodermal activity (EDA), but also reaction time (RT) are adopted to recognize different stress states of an individual. For the stress recognition, we propose an approach based on a SVM classifier (Support Vector Machine). The results obtained show that the reaction time can be used to estimate the level of stress of an individual in addition or not to the physiological signals. Besides, we discuss the feasibility of an embedded system which would realize the complete data processing. Therefore, the study of this thesis can contribute to make a portable system to recognize the stress of an individual in real time by adopting heterogeneous data like physiological signals and RT, Dans la société moderne, le stress s’avère un problème omniprésent. Un stress permanent peut entraîner divers problèmes mentaux et physiques notamment pour des personnes confrontées à des situations d'urgence comme par exemple des pompiers en intervention: il peut modifier leurs actions et les mettre en danger. Par conséquent, dans ce contexte, il est pertinent de chercher à évaluer le stress de la personne. Sur la base de cette idée, a été proposé le projet Psypocket qui vise à concevoir un système portable capable d'analyser précisément l'état de stress d'une personne en fonction de ses modifications physiologiques, psychologiques et comportementales, puis de proposer des solutions de rétroaction pour réguler cet état. Cette thèse s’inscrit dans le cadre du projet Psypocket. Nous y discutons de la faisabilité et de l'intérêt de la reconnaissance du stress à partir de données hétérogènes. Non seulement les signaux physiologiques, tels que l'électrocardiographie (ECG), l'élecchtromyographie (EMG) et l'activité électrodermale (EDA), mais aussi le temps de réaction (RT) sont adoptés pour discriminer différents états de stress d'une personne. Pour cela nous proposons une approche basée sur un classifieur SVM (Machine à Vecteurs de Support). Les résultats obtenus montrent que le temps de réaction peut-être un moyen d’estimation du niveau de stress de l’individu en complément ou non des signaux physiologiques. En outre, nous discutons de la faisabilité d'un système embarqué à même de réaliser la chaîne globale de traitement des signaux. Cette thèse contribue donc à la conception d’un système portable de reconnaissance du stress d'une personne en temps réel en adoptant des données hétérogènes, en l’occurrence les signaux physiologiques et le temps de réaction

Ameliorating the performances of speech recognition system is a challenging problem interesting recent researchers. In this paper, we compare two extraction methods of Mel Frequency Cepstral Coefficients used to represent stressed speech utterances in order to obtain best performances. The first method known as traditional is based on single window (taper) generally the Hamming window and the second one is a novel technique developed with multitapers instead of a single taper. The extracted features are then classified using the multiclass Support Vector Machines. Experimental results on the SUSAS database have shown that the multitaper MFCC features outperform the conventional MFCCs.

Stress is widely associated with increased health risks (heart & brain diseases, diabetes, cancer, behavioural disorders etc.). Moreover, a prolonged exposure to stress is known to negatively affect work performance, attitude, decision making etc. Furthermore, monitoring of stress levels and proper stress management are of crucial importance for Firefighters, Rescue crews, Police force and other high-risk professions in terms of mission success and workforce preservation. In this regard, here we overview the state-of-art in personal health monitoring systems, discuss the overall architecture and technology involved in the implementation of such functionality. A particular focus is put on the technology involved in assessment of brain activity and negative emotional states, which are linked to stress, behavioural, mental disorders etc. From application point of view we discuss the technological feasibility of stationary and mobile setups for stress level assessment and monitoring. Finally, we outline the current trends and future research directions and comment on some inherent limitations of stress level monitoring and on some challenges that remain unaddressed.

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.

Estudo transversal, analítico e quantitativo, que objetivou avaliar o clima de segurança do paciente na perspectiva dos profissionais da equipe de Enfermagem dos setores de Clínica Médica e Cirúrgica. Participaram 52 profissionais. Os dados foram coletados utilizando-se o Safety Attitudes Questionnaire. Para análises foram utilizados os testes t de Student, análise de variância e correlação de Sperman (α= 0,05). O escore geral médio do instrumento foi 66,94 pontos. O domínio com melhor escore foi “percepção da gerência” (84,60) e os domínios com menor escore foram “condições de trabalho” (60,15) e “percepção do estresse” (55,59). Houve diferenças estatisticamente significantes entre os sexos, nível de escolaridade com o domínio Clima de Segurança. Não houve relação entre a atuação profissional, vínculo empregatício e categoria profissional. Observaram-se fragilidades na percepção da equipe em relação ao clima de segurança, o que remete à necessidade de instituição de estratégias para promoção de um ambiente seguro.

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.

The Detection of stress from speech signal is gaining large attention recently. The emergence of new methods and techniques for feature extraction and classification paved the way to different solutions to detect different stress conditions using human speech and led to an in-crease in the accuracy of stress recognition. A large number of parameters are proposed for the characterization of stress in speech. Similarly numerous classifiers and machine learning algorithms are investigated for stress classification and regression. In this treatise, a recital on the commonly used databases, stress conditions, different feature extraction methods and classifiers along with some of the statistical measures as well as compensation techniques for stress detection are presented in this article. After thorough illustration of existing methodology for the task, future prospects for the work are elaborated.

In modern society, the stress of an individual has been found to be a common problem. Continuous stress can lead to various mental and physical problems and especially for the people who always face emergency situations (e.g., fireman): it may alter their actions and put them in danger. Therefore, it is meaningful to provide the assessment of the stress of an individual. Based on this idea, the Psypocket project is proposed which is aimed at making a portable system able to analyze accurately the stress state of an individual based on his physiological, psychological and behavioural modifications. It should then offer solutions for feedback to regulate this state.The research of this thesis is an essential part of the Psypocket project. In this thesis, we discuss the feasibility and the interest of stress recognition from heterogeneous data. Not only physiological signals, such as Electrocardiography (ECG), Electromyography (EMG) and Electrodermal activity (EDA), but also reaction time (RT) are adopted to recognize different stress states of an individual. For the stress recognition, we propose an approach based on a SVM classifier (Support Vector Machine). The results obtained show that the reaction time can be used to estimate the level of stress of an individual in addition or not to the physiological signals. Besides, we discuss the feasibility of an embedded system which would realize the complete data processing. Therefore, the study of this thesis can contribute to make a portable system to recognize the stress of an individual in real time by adopting heterogeneous data like physiological signals and RT, Dans la société moderne, le stress s’avère un problème omniprésent. Un stress permanent peut entraîner divers problèmes mentaux et physiques notamment pour des personnes confrontées à des situations d'urgence comme par exemple des pompiers en intervention: il peut modifier leurs actions et les mettre en danger. Par conséquent, dans ce contexte, il est pertinent de chercher à évaluer le stress de la personne. Sur la base de cette idée, a été proposé le projet Psypocket qui vise à concevoir un système portable capable d'analyser précisément l'état de stress d'une personne en fonction de ses modifications physiologiques, psychologiques et comportementales, puis de proposer des solutions de rétroaction pour réguler cet état. Cette thèse s’inscrit dans le cadre du projet Psypocket. Nous y discutons de la faisabilité et de l'intérêt de la reconnaissance du stress à partir de données hétérogènes. Non seulement les signaux physiologiques, tels que l'électrocardiographie (ECG), l'élecchtromyographie (EMG) et l'activité électrodermale (EDA), mais aussi le temps de réaction (RT) sont adoptés pour discriminer différents états de stress d'une personne. Pour cela nous proposons une approche basée sur un classifieur SVM (Machine à Vecteurs de Support). Les résultats obtenus montrent que le temps de réaction peut-être un moyen d’estimation du niveau de stress de l’individu en complément ou non des signaux physiologiques. En outre, nous discutons de la faisabilité d'un système embarqué à même de réaliser la chaîne globale de traitement des signaux. Cette thèse contribue donc à la conception d’un système portable de reconnaissance du stress d'une personne en temps réel en adoptant des données hétérogènes, en l’occurrence les signaux physiologiques et le temps de réaction

This paper investigates how speech and gestures convey stress, and how they can be used for automatic stress recognition. As a first step, we look into how humans use speech and gestures to convey stress. In particular, for both speech and gestures, we distinguish between stress conveyed by the intended semantic message (e.g. spoken words for speech, symbolic meaning for gestures), and stress conveyed by the modulation of either speech and gestures (e.g. intonation for speech, speed and rhythm for gestures). As a second step, we use this decomposition of stress as an approach for automatic stress prediction. The considered components provide an intermediate representation with intrinsic meaning, which helps bridging the semantic gap between the low level sensor representation and the high level context sensitive interpretation of behavior. Our experiments are run on an audiovisual dataset with service-desk interactions. The final goal is having a surveillance system that would notify when the stress level is high and extra assistance is needed. We find that speech modulation is the best performing intermediate level variable for automatic stress prediction. Using gestures increases the performance and is mostly beneficial when speech is lacking. The two-stage approach with intermediate variables performs better than baseline feature level or decision level fusion. © 2015 IEEE.

Automatic detection of work-related stress has attracted an increasing amount of attention from researchers from various disciplines and industries. An experiment is discussed in this paper that was designed to evaluate the efficacy of multimodal sensor measures that have often been used but not yet been systematically tested and compared with each other in previous work, such as pressure distribution sensor, physiological sensors, and an eye tracker. We used the Stroop test and information pick up task as the stressors. In the subject independent case in particular, signals from the combined (chair and floor) pressure distribution sensors, which we consider the most feasible sensors in the office environment, resulted in higher recognition accuracy rates than the physiological or eye tracker signals for the two stressors.

This paper investigates how speech and gestures convey stress, and how they can be used for automatic stress recognition. As a first step, we look into how humans use speech and gestures to convey stress. In particular, for both speech and gestures, we distinguish between stress conveyed by the intended semantic message (e.g. spoken words for speech, symbolic meaning for gestures), and stress conveyed by the modulation of either speech and gestures (e.g. intonation for speech, speed and rhythm for gestures). As a second step, we use this decomposition of stress as an approach for automatic stress prediction. The considered components provide an intermediate representation with intrinsic meaning, which helps bridging the semantic gap between the low level sensor representation and the high level context sensitive interpretation of behavior. Our experiments are run on an audiovisual dataset with service-desk interactions. The final goal is having a surveillance system that would notify when the stress level is high and extra assistance is needed. We find that speech modulation is the best performing intermediate level variable for automatic stress prediction. Using gestures increases the performance and is mostly beneficial when speech is lacking. The two-stage approach with intermediate variables performs better than baseline feature level or decision level fusion. © 2015 IEEE.

This paper investigates gender differences in stress recognition in human computer interaction (HCI) for both objective (i.e., skin conductance) and subjective (i.e., valence-arousal VA ratings) metrics. To this end, 31 healthy participants, 18 females, performed five HCI tasks, while their skin conductance was recorded. These selected HCI tasks were the ones listed as the most stressful, by a group of typical computer users, who were involved in a face to face pre-experiment interview for the identification of stressful cases in computer interaction. After each task, participants rated their interaction experience using the valence-arousal scale. The collected data were split based on participants' gender. Skin conductance signals were analyzed using seven popular machine learning classifiers. In both groups the best stress recognition accuracy for all tasks was achieved by Linear Discriminant Analysis LDA; Males: Mean=94.8% and SD=1.5%, Females: Mean=98.9% and SD=0.3%. Self-reported data analysis revealed a significant difference on how both genders communicate their emotions using the arousal scale. Our findings tend to suggest that gender does not affect skin conductance data during subtle HCI tasks. However subjective ratings such as arousal of emotional experience must be utilized carefully.

Thirty-one healthy participants performed five stressful HCI tasks (stimuli) while their skin conductance signals were monitored. The selected interaction tasks were most frequently listed as stressful by 15 interviewees, who were typical computer users. At the end of each task, participants expressed their perceived emotional experience using the dimensional Valence-Arousal (VA) rating space. The obtained VA ratings were used to define nine rectangular regions in the VA space, labeled as "stress". Next, five popular machine learning classifiers were employed to identify stress based on the associated skin conductance signals per tested region. Results showed sufficient cross-region stress recognition accuracy; L-SVM: Mean=62.3%, SD=9.3%. Our findings support that the VA scale may be used for stress self-assessments in the context of subtle interaction events, which are typically expected in most HCI tasks.

application/pdf, 論文(Article), 低出生体重児の養育者である母親が、低出生体重児の持つ生活上の問題や状態をどのように認知し、母親の心理的側面に影響を与えているかに着目した。Lazarus のストレス認知理論の概念枠組みを参考にし、ストレス認知→ストレス反応→QOLの仮説を導きだした。ストレス認知については育児負担感、ストレス反応はバーンアウト、そしてQOLは心理的QOLを測定尺度として採用し、共分散構造分析によって因果関係を検証した。データ分析の結果、低出生体重児を育児している母親の心理的QOLは、育児負担感の「活動制限の認知」から直接的に、あるいは、バーンアウトの「情緒的疲弊」を経由して間接的に負の影響を受け、同時に育児負担感の「否定的感情の認知」からバーンアウトの「情緒的疲弊」を経由して間接的に負の影響を受けることが実証された。, The purpose is to make clear whether to influence a side concerning mother 's mental subjectivity in life which the mother who is the low birth weight infant's main caregiver has a low birth weight infant to and condition were recognized how. We find the hypothesis of the stress recognition→stress reaction→QOL referring to the stress recognition theory of Lazarus. It was verified by the co-variance structure analysis with a measurement measure as the stress recognition for a feeling of the child care burden, the stress reaction for burn out and QOL for mental QOL. The mental QOL of the mother who cares a low birth weight infant is influenced directly from the recognition of the activities limitation, of a feeling of the child care burden, the negative influence is taken indirectly from the emotional exhaustion of burn out from the recognition of the negative emotion of a feeling of the child care burden at the same time as a result of the data analysis. It was proved that the negative influence was taken indirectly through the emotional exhaustion.

Stress is universally known to be a contributing factor in developing of many diseases. This work focuses on developing a user-independent and user-dependent stress recognition system using five physiological signals that are electromyogram, galvanic skin response, skin temperature, blood volume pulse and respiratory response. Emotional data is collected from 33 subjects by using Stroop game. These are processed using Discrete Wavelet Transform (DWT) and Empirical Mode Decomposition (EMD). Also, decomposition of physiological signals into intrinsic mode functions (IMFs) by the EMD method is done. With the Support Vector Machine (SVM), the classification results show a better accuracy using the DWT method compared to those of the EMD method. For the user-independent study an overall classification accuracy of 60.9% in stress recognition is reached whereas for the user-dependent study an overall classification accuracy of 80% is achieved. In addition, overall recognition rates attain 100% when using the DWT.

This paper reports an experiment for stress recognition in human-computer interaction. Thirty-one healthy participants performed five stressful HCI tasks and their skin conductance signals were monitored. The selected tasks were most frequently listed as stressful by 15 typical computer users who were involved in pre-experiment interviews asking them to identify stressful cases of computer interaction. The collected skin conductance signals were analyzed using seven popular machine learning classifiers. The best stress recognition accuracy was achieved by the cubic support vector machine classifier both per task (on average 90.8 %) and for all tasks (Mean = 98.8 %, SD = 0.6 %). This very high accuracy demonstrates the potentials of using physiological signals for stress recognition in the context of typical HCI tasks. In addition, the results allow us to move on a first integration of the specific stress recognition mechanism in PhysiOBS, a previously-proposed software tool that supports researchers and practitioners in user emotional experience evaluation.

This study proposed a novel method of stress recognition using photopletysmogram (PPG) signal. PPG devices are now widely used because it is convenient, low powered, cheap and also easy to handle due to its small size. A total of 5 subjects were involved in this study. The PPG signals were taken in resting condition using pulse oximetry. The subject then goes through a stressor test in order to record the physiological changes. The data were collected before and after the test was conducted and later extracted. These samples were then categorised using classification techniques to differentiate between normal and stress condition. Based on the experimentation results, the systolic peak value differences of normal and stress conditions are evident.Therefore, the outcome of this study suggest the reliability of implementing PPG signal for stress recognition.

Because the term stress is used, most often subjectively, with variousmeanings, this paper first attempts to clarify the physiological definition,andthe appropriate terms as responses in different situations. The flexibility ofnormal metabolism allows the development of responses to environmental changeswhich fluctuate regularly and predictably over daily and seasonal cycles. Thusevery deviation of a factor from its optimum does not necessarily result instress. Stress begins with a constraint or with highly unpredictablefluctuations imposed on regular metabolic patterns that cause bodily injury,disease, or aberrant physiology. Stress is the altered physiological conditioncaused by factors that tend to alter an equilibrium. Strain is any physicaland/or chemical change produced by a stress, i.e. every established condition,which forces a system away from its thermodynamic optimal state. The papersecondly summarises the Strasser's state-change concept which is preciselythat suboptimality is the driving force for acclimation (genotype level) oradaptation (population level) to stress. The paper continues with the actualknowledge on the mechanisms of stress recognition and cell signalling. Briefly:plasma membranes are the sensors of environmental changes; phytohormones andsecond messengers are the transducers of information from membranes tometabolism; carbon balance is the master integrator of plant response; betwixtand between, some genes are expressed more strongly, whereas others arerepressed. Reactive oxygen species play key roles in up- and down-regulation ofmetabolism and structure. The paper shows finally that the above concepts canbeapplied to plant tissue cultures where the accumulating physiological andgenetical deviations (from a normal plant behaviour) are related to thestressing conditions of the in vitro culture media and ofthe confined environment. The hyperhydrated state of shoots and the cancerousstate of cells, both induced under conditions of stress in invitro cultures, are identified and detailed, because they perfectlyillustrate the stress-induced state-change concept. It is concluded that stressresponses include either pathologies or adaptive advantages. Stress may thuscontain both destructive and constructive elements : it is a selection factoraswell as a driving force for improved resistance and adaptive evolution.

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 adversely affects the wellbeing of commercial chickens, and comes with an economic cost to the industry that cannot be ignored. In this paper, we first develop an inexpensive and non-invasive, automatic online-monitoring prototype that uses sound data to notify producers of a stressful situation in a commercial poultry facility. The proposed system is structured hierarchically with three binary-classifier support vector machines. First, it selects an optimal acoustic feature subset from the sound emitted by the laying hens. The detection and classification module detects the stress from changes in the sound and classifies it into subsidiary sound types, such as physical stress from changes in temperature, and mental stress from fear. Finally, an experimental evaluation was performed using real sound data from an audio-surveillance system. The accuracy in detecting stress approached 96.2%, and the classification model was validated, confirming that the average classification accuracy was 96.7%, and that its recall and precision measures were satisfactory.

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.

This study aimed to measure safety culture, examine variations among neonatal intensive care units (NICUs), and assess the associations with caregiver characteristics.A cross-sectional design was used, utilizing the Arabic version of the Safety Attitudes Questionnaire, administered to all 305 nurses and physicians working in the 16 NICUs in the West Bank.There were 204 participants, comprising of mainly nurses (80.4%), women (63%), 30 years or younger (62.6%), holding a bachelor's degree or more (66.7%), and with at least 5 years of experience in the profession (60.3%). Safety Attitudes Questionnaire mean domain scores ranged from 71.22 for job satisfaction to 63 for stress recognition on a 100-point scale; the scores varied significantly among NICUs (P.05). About 85% of the participants rated the safety grade either excellent or very good; 71.0% did not report any event in the past year.We found large variations in safety culture within and between a comprehensive sample of Palestinian NICUs. The findings suggest the need for a customized approach that builds on existing strengths and targets areas of opportunities for improvement to optimize health care delivery to the most vulnerable of patients, sick newborns in the NICU setting.

Stressis a major problem facing our world today and affects everyday lives providing motivation to develop an objective understanding of stress during typicalactivities. Physiological and physical response signals showing symptoms for stress can be used to provide hundreds of features. This encounters the problem of selecting appropriate features for stress recognition from a set of features that may include irrelevant, redundant or corrupted features. In addition, there is also a problem for selecting an appropriate computational classification model with optimal parameters to capture general stress patterns. The aim of this paper is to determine whether stress can be detected from individual-independent computational classification models with a genetic algorithm (GA) optimization scheme from sensor sourced stress response signals induced by reading text. The GA was used to select stress features, select a type of classifier and optimize the classifier's parameters for stress recognition. The classification models used were artificial neural networks (ANNs) and support vector machines (SVMs). Stress recognition rates obtained from an ANN and a SVM without a GA were 68% and 67% respectively. With a GA hybrid, the stress recognition rate improved to 89%. The improvement shows that a GA has the capacity to select salient stress features and define an optimal classification model with optimized parameter settings for stress recognition.

Background Effective communication skills in the operating room (OR) promote team behaviour and usually result in good patient outcomes. Communication and collaboration constitutes an integral role during this process both with patients and with inter-professionals when working in such a highly specialised setting as an OR. Patient and staff safety in the OR has never been investigated in Cyprus before. Methods One of the most often used tools to assess safety culture in an organisation is the Safety Attitudes Questionnaire (SAQ) (The Evidence Centre, 2011 & Sexton et al, 2006). SAQ is a psychometric instrument used for evaluating healthcare providers and especially operating nurses’ attitudes regarding patient safety and climate. Six major factors are measured through the use of modified SAQ: teamwork climate, job satisfaction, management perception, safety climate, working conditions and stress recognition. A random sample of 112 OR nurses was studied from Makarios III Hospital which is one of the two major public hospitals in Nicosia. Results The sample was comprised of 30 males and 82 females: the majority was working as permanent staff in the hospital and their experience varied from 0–25 years both in total as well as in the specific hospital. Overall participants believed that issues related to guidelines, teamwork and patient safety need further improvements. Males have significantly higher scores regarding experience with safety issues in the OR compared to their female counterparts. Interestingly, the lowest scores regarding nurses’ notions and opinions regarding OR safety were expressed by the most experienced participants, who had more than 25 years of work experience. This is an indication of the need for further improvements in the in-service training which currently takes place in the Cyprus health system. Conclusions More experienced staff might be in need for education regarding more recent safety procedures and techniques followed in the OR.

Background: The traditional medical Oath of ‘first do no harm’ is something that is very rarely breached either intentionally by a doctor, nurse or other health practitioner. Resident is health workers who perform services directly to patients that’s why their knowledge and attitudes towards patient safety are very useful Objective: To determine the relationship of the level of knowledge with the attitudes about patient safety in obstetrics and gynecology resident while services in delivery room at Dr DR Sardjito hospital. Method: Descriptive methods with cross sectional approach, accompanied by a qualitative research The study involved 90 residents of Obstetrics and Gynecology at Sardjito Hospital Yogyakarta , by filling out a questionnaire. Knowledge assessed based on the number of correct answers and categorized into good and bad. Attitudes toward patient safety was assessed using a Safety Attitude Questionnaire (SAQ) labor and delivery version. Reliability was measured using Cronbach’s alpha. The qualitative data collection (interviews) conducted to support the descriptive data. Result and Discussion: Cronbach’s Alpha value for SAQ= 0.862, and the question of knowledge = 0.702. Between 77 responden who had good level of knowlage, only 53,2% had a good level of attitudes, and between 13 who has bad level of knowlage, there were 10 responden with good level of attitudes (76,9%) (RP 0,69;95%CI 0,44-1,04; p= 0,11). Elements of stress recognition on the attitude were getting the lowest average value (3.50 ± 0.56). From the qualitative data were known that the resident had desire for a reduction in working hours as well as improved communication and collaboration between teams. Althogh most of resident had a good level of knowledge and attitudes about patient safety, there were no meaning because of p value was >0,05 (OR 2,69;95%CI 0,66-10,9; p= 0,17). Conclusion: Clinically, a good level of knowledge about patien safety improving a good level of attitude towards patient safety. Keywords: Knowledge- attitude- patient safety- resident obstetrics

This paper deals with speech signal under psychological stress. The investigation into the speaker’s stress is based on statistical analysis of smooth vowel spectra. In total, fifteen features based on formants, antiformants and inflexion points of spectral envelope were analysed and evaluated. The most effective features for stress detection across all speakers were as follows: frequency of the second formant of /a/, frequency of the first formant of /i/, and lower part of bandwidth of the third formant of /u/ and /a/. The results indicate that speakers tend to follow an individual trend and speaker dependent stress recognition might be more appropriate. DOI: http://dx.doi.org/10.5755/j01.eee.18.10.3059

The aim of this study was to automatically identify the iris and pupil of the eye in the video stream and to parameterize the identified structures in order to make assumptions if the subjected is stressed or not. During tests subjects were given a number of issues which they had to respond by selecting only one correct answer. Visual material was gathered using a helmet-fitted stationary near-infrared camera that recorded iris and pupil of the eye reactions to stimuli. Subsequently it was made an automatic iris and pupil recognition and approximation by curves in the gathered sequence of images. Each change in the pupil size is described by different time series length. Thus, it is impossible to compare the obtained data using the Euclidean distance measures. For this reason, the metrics based on periodograms was used to compare the data series. The differences calculated between the eye pupil reaction to stimuli and question show-up time was introduced in multidimensional scaling algorithms for dimension reduction. It was noticed that the stimulus to the false answers tend to cluster. DOI: http://dx.doi.org/10.5755/j01.itc.41.1.1206