Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection.

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90-15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility-privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements.

Strategies for Reliable Stress Recognition: A Machine Learning Approach Using Heart Rate Variability Features.

Stress recognition, particularly using machine learning (ML) with physiological data such as heart rate variability (HRV), holds promise for mental health interventions. However, limited datasets in affective computing and healthcare research can lead to inaccurate conclusions regarding the ML model performance. This study employed supervised learning algorithms to classify stress and relaxation states using HRV measures. To account for limitations associated with small datasets, robust strategies were implemented based on methodological recommendations for ML with a limited dataset, including data segmentation, feature selection, and model evaluation. Our findings highlight that the random forest model achieved the best performance in distinguishing stress from non-stress states. Notably, it showed higher performance in identifying stress from relaxation (F1-score: 86.3%) compared to neutral states (F1-score: 65.8%). Additionally, the model demonstrated generalizability when tested on independent secondary datasets, showcasing its ability to distinguish between stress and relaxation states. While our performance metrics might be lower than some previous studies, this likely reflects our focus on robust methodologies to enhance the generalizability and interpretability of ML models, which are crucial for real-world applications with limited datasets.

Mediating Effect of Stress Recognition on the Effect of Generalized Anxiety Disorder on Smartphone Dependence.

The widespread adoption of the smartphone has led to both positive and negative consequences for adolescents' mental health. This study examines the interplay between smartphone dependence (SPD), generalized anxiety disorder (GAD), and various mental health outcomes among Korean adolescents. Data from the 16th Adolescence Health Behavior Survey (2020), including 54,948 middle and high school students, were analyzed. Adolescents were categorized into three groups based on SPD severity. The GAD-7 scale assessed anxiety, and other factors such as subjective health recognition, happiness, weight control efforts, and body mass index (BMI) were considered. Adolescents with higher SPD exhibited lower academic performance, decreased happiness, and increased perception of stress. GAD levels were positively correlated with SPD, with higher SPD linked to more severe GAD symptoms. Additionally, higher SPD was associated with increased loneliness, sadness, and suicidal thoughts, plans, and attempts as well as a greater likelihood of habitual drug use. Gender differences revealed that females were more prone to sadness, hopelessness, and suicidal thoughts, while males exhibited higher rates of drug use. This study highlights the complex relationship between SPD, GAD, and mental health outcomes among Korean adolescents. Stress recognition was found to mediate the association between GAD and SPD. The process-macro result of the total effect between SPD on GAD and the direct effect of the SPD pathway on GAD was significant; thus, the stress recognition was mediated. Effective interventions should target stress management, especially among adolescents with high smartphone dependence, to mitigate the risk of mental health issues. These findings underscore the importance of addressing smartphone dependence and its impact on the mental well-being of adolescents.

Medical staff's emotional exhaustion and its relationship with patient safety dimensions.

PURPOSE: Medical staff's emotional exhaustion increases cynical attitudes and behaviors about work and patients and leads medical staff to become detached from work. This may decrease patients' trust and satisfaction and even endanger patients' lives. There is a need to examine the critical factors affecting the medical staff's emotional exhaustion by investigating its relationship with the patient-safety dimensions based on the safety attitudes questionnaire (SAQ). DESIGN/METHODOLOGY/APPROACH: A case study is conducted from the viewpoints of physicians and nurses to examine the relationship between emotional exhaustion and six dimensions of the SAQ from 2016 to 2020 from a regional teaching hospital in Taiwan. Linear regression with forward selection is employed. Six dimensions of the SAQ are the independent variables, whereas emotional exhaustion is the dependent variable for each year. FINDINGS: Stress recognition is the most important variable to influence emotional exhaustion negatively, while job satisfaction is the second important variable to affect emotional exhaustion positively from 2016 to 2020. On the contrary, working conditions do not influence emotional exhaustion in this hospital from medical staff's viewpoints. ORIGINALITY/VALUE: This study uses longitudinal data to find that both stress recognition and job satisfaction consistently influence emotional exhaustion negatively and positively, respectively, in this five-year period. The third dimension to impact emotional exhaustion varies from time to time. Thus, the findings from a cross-sectional study might be limited. The authors' findings show that reducing stress recognition and enhancing job satisfaction can lead to the improvement of emotional exhaustion from medical staff's viewpoints, which should be monitored by hospital management.

Mental Stress Classification Based on Selected Electroencephalography Channels Using Correlation Coefficient of Hjorth Parameters.

Electroencephalography (EEG) signals offer invaluable insights into diverse activities of the human brain, including the intricate physiological and psychological responses associated with mental stress. A major challenge, however, is accurately identifying mental stress while mitigating the limitations associated with a large number of EEG channels. Such limitations encompass computational complexity, potential overfitting, and the prolonged setup time for electrode placement, all of which can hinder practical applications. To address these challenges, this study presents the novel CCHP method, aimed at identifying and ranking commonly optimal EEG channels based on their sensitivity to the mental stress state. This method's uniqueness lies in its ability not only to find common channels, but also to prioritize them according to their responsiveness to stress, ensuring consistency across subjects and making it potentially transformative for real-world applications. From our rigorous examinations, eight channels emerged as universally optimal in detecting stress variances across participants. Leveraging features from the time, frequency, and time-frequency domains of these channels, and employing machine learning algorithms, notably RLDA, SVM, and KNN, our approach achieved a remarkable accuracy of 81.56% with the SVM algorithm outperforming existing methodologies. The implications of this research are profound, offering a stepping stone toward the development of real-time stress detection devices, and consequently, enabling clinicians to make more informed therapeutic decisions based on comprehensive brain activity monitoring.

Real-World Driver Stress Recognition and Diagnosis Based on Multimodal Deep Learning and Fuzzy EDAS Approaches.

Mental stress is known as a prime factor in road crashes. The devastation of these crashes often results in damage to humans, vehicles, and infrastructure. Likewise, persistent mental stress could lead to the development of mental, cardiovascular, and abdominal disorders. Preceding research in this domain mostly focuses on feature engineering and conventional machine learning approaches. These approaches recognize different levels of stress based on handcrafted features extracted from various modalities including physiological, physical, and contextual data. Acquiring good quality features from these modalities using feature engineering is often a difficult job. Recent developments in the form of deep learning (DL) algorithms have relieved feature engineering by automatically extracting and learning resilient features. This paper proposes different CNN and CNN-LSTSM-based fusion models using physiological signals (SRAD dataset) and multimodal data (AffectiveROAD dataset) for the driver's two and three stress levels. The fuzzy EDAS (evaluation based on distance from average solution) approach is used to evaluate the performance of the proposed models based on different classification metrics (accuracy, recall, precision, F-score, and specificity). Fuzzy EDAS performance estimation shows that the proposed CNN and hybrid CNN-LSTM models achieved the first ranks based on the fusion of BH, E4-Left (E4-L), and E4-Right (E4-R). Results showed the significance of multimodal data for designing an accurate and trustworthy stress recognition diagnosing model for real-world driving conditions. The proposed model can also be used for the diagnosis of the stress level of a subject during other daily life activities.

Effects of water stress and fertilizer stress on maize growth and spectral identification of different stresses.

Water stress and fertilizer stress have a significant impact on the growth and yield of maize. In order to improve the timeliness and accuracy of irrigation and fertilizer application, it is crucial to monitor water stress and fertilizer stress rapidly and accurately. This would help in conserving water and fertilizer resources and ensuring a stable maize yield. To this end, pot experiments were set up to explore the growth differences and photosynthetic properties of maize under water stress and fertilizer stress. The hyperspectral technology was used to construct the spectral indexes that can distinguish stress types, and the classification algorithm was combined to identify stress types. The research has shown that the plant height, basal diameter, leaf area, and photosynthetic properties of maize decreased with an increase in drought stress. However, rewatering could compensate for drought stress. Furthermore, fertilizer stress also affected water uptake by plants, and high nitrogen stress had a significant negative effect on the growth of maize plants. We employed a combination of spectral indexes and the support vector machine (SVM) classification algorithm in a stepwise manner to identify stress types. Using the training dataset, we constructed six classifiers for distinguishing stress types, including the SVM classifier, K-nearest neighbor (KNN) classifier, naive Bayes (NB) classifier, decision tree (DT) classifier, random forest (RF) classifier, and AdaBoost classifier. Our results showed that the RF and AdaBoost classifiers obtained stable results in stress type differentiation, achieving accurate identification of unstressed, water stressed, and fertilizer stressed maize plants. This is expected to provide a solid basis and reference for monitoring crop stress types in agricultural fields.

Evaluating Visual Photoplethysmography Method.

Regular monitoring of common physiological signs, including heart rate, blood pressure, and oxygen saturation, can be an effective way to either prevent or detect many kinds of chronic conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 32% of all deaths worldwide are from CVDs. In addition, stress-related illnesses cost $190 billion in healthcare costs per year. Currently, contact devices are required to extract most of an individual's physiological information, which can be uncomfortable for users and can cause discomfort. However, in recent years, remote photoplethysmography (rPPG) technology is gaining interest, which enables contactless monitoring of the blood volume pulse signal using a regular camera, and ultimately can provide the same physiological information as a contact device. In this paper, we propose a benchmark comparison using a new multimodal database consisting of 56 subjects where each subject was submitted to three different tasks. Each subject wore a wearable device capable of extracting photoplethysmography signals and was filmed to allow simultaneous rPPG signal extraction. Several experiments were conducted, including a comparison between information from contact and remote signals and stress state recognition. Results have shown that in this dataset, rPPG signals were capable of dealing with motion artifacts better than contact PPG sensors and overall had better quality if compared to the signals from the contact sensor. Moreover, the statistical analysis of the variance method had shown that at least two heart-rate variability (HRV) features, NNi 20 and SAMPEN, were capable of differentiating between stress and non-stress states. In addition, three features, inter-beat interval (IBI), NNi 20, and SAMPEN, were capable of differentiating between tasks relating to different levels of difficulty. Furthermore, using machine learning to classify a "stressed" or "unstressed" state, the models were able to achieve an accuracy score of 83.11%.

Towards a Non-Contact Method for Identifying Stress Using Remote Photoplethysmography in Academic Environments.

Stress has become a common condition and is one of the chief causes of university course disenrollment. Most of the studies and tests on academic stress have been conducted in research labs or controlled environments, but these tests can not be extended to a real academic environment due to their complexity. Academic stress presents different associated symptoms, anxiety being one of the most common. This study focuses on anxiety derived from academic activities. This study aims to validate the following hypothesis: by using a non-contact method based on the use of remote photoplethysmography (rPPG), it is possible to identify academic stress levels with an accuracy greater than or equal to that of previous works which used contact methods. rPPG signals from 56 first-year engineering undergraduate students were recorded during an experimental task. The results show that the rPPG signals combined with students' demographic data and psychological scales (the State-Trait Anxiety Inventory) improve the accuracy of different classification methods. Moreover, the results demonstrate that the proposed method provides 96% accuracy by using K-nearest neighbors, J48, and random forest classifiers. The performance metrics show better or equal accuracy compared to other contact methods. In general, this study demonstrates that it is possible to implement a low-cost method for identifying academic stress levels in educational environments.

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information.

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

Stress Evaluation in Simulated Autonomous and Manual Driving through the Analysis of Skin Potential Response and Electrocardiogram Signals.

The evaluation of car drivers' stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver's stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving.

How Laboratory Experiments Can Be Exploited forMonitoring Stress in the Wild: A Bridge BetweenLaboratory and Daily Life.

Chronic stress leads to poor well-being, and it has effects on life quality and health. Societymay have significant benefits from an automatic daily life stress detection system using unobtrusivewearable devices using physiological signals. However, the performance of these systems is notsufficiently accurate when they are used in unrestricted daily life compared to the systems testedin controlled real-life and laboratory conditions. To test our stress level detection system thatpreprocesses noisy physiological signals, extracts features, and applies machine learning classificationtechniques, we used a laboratory experiment and ecological momentary assessment based datacollection with smartwatches in daily life. We investigated the effect of different labeling techniquesand different training and test environments. In the laboratory environments, we had more controlledsituations, and we could validate the perceived stress from self-reports. When machine learningmodels were trained in the laboratory instead of training them with the data coming from daily life,the accuracy of the system when tested in daily life improved significantly. The subjectivity effectcoming from the self-reports in daily life could be eliminated. Our system obtained higher stresslevel detection accuracy results compared to most of the previous daily life studies.

Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study.

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.

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.

A Transductive Model-based Stress Recognition Method Using Peripheral Physiological Signals.

Existing research on stress recognition focuses on the extraction of physiological features and uses a classifier that is based on global optimization. There are still challenges relating to the differences in individual physiological signals for stress recognition, including dispersed distribution and sample imbalance. In this work, we proposed a framework for real-time stress recognition using peripheral physiological signals, which aimed to reduce the errors caused by individual differences and to improve the regressive performance of stress recognition. The proposed framework was presented as a transductive model based on transductive learning, which considered local learning as a virtue of the neighborhood knowledge of training examples. The degree of dispersion of the continuous labels in the y space was also one of the influencing factors of the transductive model. For prediction, we selected the epsilon-support vector regression (e-SVR) to construct the transductive model. The non-linear real-time features were extracted using a combination of wavelet packet decomposition and bi-spectrum analysis. The performance of the proposed approach was evaluated using the DEAP dataset and Stroop training. The results indicated the effectiveness of the transductive model, which had a better prediction performance compared to traditional methods. Furthermore, the real-time interactive experiment was conducted in field studies to explore the usability of the proposed framework.

Deep ECGNet: An Optimal Deep Learning Framework for Monitoring Mental Stress Using Ultra Short-Term ECG Signals.

BACKGROUND: Stress recognition using electrocardiogram (ECG) signals requires the intractable long-term heart rate variability (HRV) parameter extraction process. This study proposes a novel deep learning framework to recognize the stressful states, the Deep ECGNet, using ultra short-term raw ECG signals without any feature engineering methods. METHODS: The Deep ECGNet was developed through various experiments and analysis of ECG waveforms. We proposed the optimal recurrent and convolutional neural networks architecture, and also the optimal convolution filter length (related to the P, Q, R, S, and T wave durations of ECG) and pooling length (related to the heart beat period) based on the optimization experiments and analysis on the waveform characteristics of ECG signals. The experiments were also conducted with conventional methods using HRV parameters and frequency features as a benchmark test. The data used in this study were obtained from Kwangwoon University in Korea (13 subjects, Case 1) and KU Leuven University in Belgium (9 subjects, Case 2). Experiments were designed according to various experimental protocols to elicit stressful conditions. RESULTS: The proposed framework to recognize stress conditions, the Deep ECGNet, outperformed the conventional approaches with the highest accuracy of 87.39% for Case 1 and 73.96% for Case 2, respectively, that is, 16.22% and 10.98% improvements compared with those of the conventional HRV method. CONCLUSIONS: We proposed an optimal deep learning architecture and its parameters for stress recognition, and the theoretical consideration on how to design the deep learning structure based on the periodic patterns of the raw ECG data. Experimental results in this study have proved that the proposed deep learning model, the Deep ECGNet, is an optimal structure to recognize the stress conditions using ultra short-term ECG data.

Safety culture and safety attitudes of nurses in the National University Hospital

Purpose@#Patient safety issues pose a great burden worldwide. However, there is still inadequate data on the burden of Patient Safety issues in the Philippines to specifically address institutional and national concerns through directed programs, policies, and interventions. This current study aims to describe the safety culture and attitudes of nurses of the National University Hospital in Manila, Philippines.@*Design@#This study used a non-experimental design that assessed the safety culture and safety attitudes of nurses from the general units of the National University Hospital (NUH) using two assessment tools: the Agency for Healthcare Research and Quality Hospital Survey on Patient Safety Culture (AHRQ-HSOPS) and the Safety Attitudes Questionnaire- Short Form (SAQ). Ethical approval was granted from both the university and the hospital ethics review boards.@*Methods@#Stratified random sampling was used to ensure representation of staff nurses and nurse administrators. A self-administered survey that included the two tools was translated to Filipino and administered to 200 nurses. Percent of positive responses were obtained to describe the safety culture and attitudes as prescribed by toolkits of AHRQ-HSOPS and SAQ.@*Findings@#The overall survey response rate yielded 86.77%. Nurses from the National University Hospital displayed both positive Safety Culture and Safety Attitudes based on AHRQ-HSOPS and SAQ. Dimensions that garnered the highest positive perceptions in Safety Culture were Organizational Learning and Teamwork while the lowest were Hospital Handoffs and Non-Punitive Response to Error. On the other hand, dimensions on Safety Awareness that received the highest positive perceptions were Teamwork and Safety Climate while the lowest was Stress Recognition. Perceptions of nurses also varied significantly across ranks in position titles and work settings.@*Conclusions@#There are identifiable dimensions that can be improved in both Safety Culture and Safety Attitude that can have a positive impact on nurses and potentially impact nurse-patient and hospital-sensitive outcomes through hospital-wide improvement programs.

Safety culture and safety attitudes of nurses in the National University Hospital

PURPOSE: Patient safety issues pose a great burden worldwide. However, there is still inadequate data on the burden of Patient Safety issues in the Philippines to specifically address institutional and national concerns through directed program, policies, and interventions. This current study aims to describe the safety culture and attitudes of nurses of the National University Hospital in Manila, Philippines.DESIGN: This study used non-experimental design that assessed the safety culture and safety attitudes of nurses from the general units of the National University Hospital (NUH) using two assessment tools: the Agency for Healthcare Research and Quality Hospital Survey on Patient Safety Culture (AHRQ-HSOPS) and the Safety Attitudes Questionnaire- Short Form (SAQ). Ethical approval was granted from both the university and the hospital ethics review boards. METHODS: Stratified random sampling was used to ensure representation of staff nurses and nurse administrators. A self-administered survey that included the two tools was translated to Filipino and administered to 200 nurses. Percent of positive responses were obtained to describe the safety culture and attitudes as prescribed by toolkits of AHRQ-HSOPS and SAQ.FINDINGS: The overall survey response rate yielded 86.77%. Nurses from the National University Hospital displayed both positive Safety Culture and Safety Attitudes based on AHRQ-HSOPS and SAQ. Dimensions that garnered the highest positive perceptions in Safety Culture were Organizational Learning and Teamwork while the lowest were Hospital Handoffs and Non-Punitive Response to Error. On the other hand, dimensions on safety Awareness that received the highest positive perceptions were Teamwork and Safety Climate while the lowest was Stress Recognition. Perceptions of nurses also varied significantly across ranks in position titles and work settings. CONCLUSIONS: There are identifiable dimensions that can be improved in both Safety Culture and Safety Attitude that can have a positive impact on nurses and potentially impact nurse-patient and hospital-sensitive outcomes through hospital-wide improvement programs.