Human Stress Level Research Articles

Integrated mental stress smartwatch based on sweat cortisol and HRV sensors

Mental stress, a human's common emotion that is difficult to recognize and describe, can give rise to serious psychological disorders. Skin and sweat are easily accessible sources of biomarkers and bio-signals that contain information about mental stress. It is challenging for current wearable devices to monitor psychological stress in real-time with a non-invasive manner. Therefore, we have developed a smartwatch integrated with a sweat cortisol sensor and a heart rate variation (HRV) sensor. This smartwatch can simultaneously record the cortisol levels in sweat and HRV index in real time over a long period. The cortisol sensors based on organic electrochemical transistor (OECT) are fabricated by utilizing the Prussian-blue (PB) doped molecular imprinting polymer (MIP) modified gate electrode. The sensor signal current will decrease following the combination of sweat cortisol, due to the blocking of the PBMIP conductive path, demonstrating good sensitivity, selectivity, and stability. The HRV sensor is manufactured by a photoplethysmography method. We have integrated the two sensors into a wearable smartwatch that can match well with the mobile phone APP and the upper computer software. Through the use of this smartwatch, we have observed a negative correlation between cortisol levels in sweat and the HRV index in short-term stressful environments. Our research presents a great progress in real-time and non-invasive monitoring human's stress levels, which promotes not only the stress management, but also better psychological research.

PECULIARITIES OF ADAPTATION AND COURSE OF POST-TRAUMATIC STRESS DISORDER IN SERVICEMEN DEPLOYED TO INTENSE COMBAT ZONES

In the context of war, post-traumatic stress disorder (PTSD) emerges as one of the most prevalent psychological conditions, particularly among military personnel deployed in combat zones. The primary triggers for PTSD in military personnel often include the loss of comrades or the necessity to use lethal weapons, resulting in significant consequences. For PTSD to manifest, individuals must undergo a traumatic experience that exceeds normal human stress levels and leads to distress. The aim of this study is to investigate the course of PTSD in military personnel who have been exposed to intense combat operations and to explore their maladaptive manifestations. The study comprised 38 patients diagnosed with PTSD (F43.1) according to the ICD-10, who received treatment at the Regional Mental Health Care Institution for the Protection of the Armed Forces. All patients provided informed consent to participate in the study. A structured clinical diagnostic interview known as CAPS-DX was applied in this study. Developed based on the DSM-IV, this interview assesses the symptoms of posttraumatic stress disorder (PTSD), the duration of the disorder, and the degree of impairment of social functioning. The CAPS-DX scale provided insights into the presence of PTSD symptoms both currently and throughout the individual's life following psychotrauma. To diagnose psychosocial maladjustment and its characteristics, we employed the Psychosocial Maladjustment Scale by L. Herasymenko (2018). Clinical and anamnestic examinations revealed that 86% of servicemen had experienced multiple traumatic events, indicating a history of several psychotraumas. The study identified that the primary psychopathological manifestations of PTSD did not differ significantly from the traditional course of the disorder, but they were more intense and pronounced in this group of servicemen. The study has demonstrated that servicemen with combat experience and PTSD exhibited reduced levels of psychosocial adaptation. Their adaptive resources and capabilities, as well as their neuropsychological stability, were significantly diminished, along with a decrease in their communication abilities. Based on the overall level and quality of social and psychological adaptation, three levels of adaptation/maladaptation were distinguished among servicemen with combat-related PTSD: sufficient, partial, or maladaptation. It should be noted that the levels of adaptation were correlated with the severity of anxiety. Servicemen with sufficient adaptation typically exhibited low levels of anxiety, while those with partial adaptation showed an average level, and those with maladaptation displayed high levels of anxiety. The study revealed a spectrum of psychopathological manifestations in servicemen who were in the combat zone, ranging from semiotic complexes resembling panic attacks to psychogenic catalepsy. Furthermore, it was found that servicemen with combat experience and post-traumatic stress disorder had reduced levels of psychosocial adaptation, which correlated with the severity of anxiety.

HUMAN STRESS LEVEL IDENTIFICATION USING MACHINE LEARNING ALGORITHMS BASED ON SLEEPING PATTERNS

Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study's main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. The obtained dataset includes various sleep habits and stress levels. Six machine learning techniques, including Multilayer Perception (MLP), Random Forest, Support Vector Machine (SVM), Decision Trees, Naïve Bayes and Logistic Regression were utilized in the classification level after the data had been preprocessed in order to compare and obtain the most accurate results. Based on the experiment results, it can be concluded that the Decision tree algorithm, when used to classify the data, can do so with 93.27% accuracy, high precision, recall, and f1measure values, as well as the lowest mean absolute error (MAE) and root mean squared error rates (RMSE). We can estimate human stress levels using the study's findings, and we can address pertinent problems as soon as possible.

HUMAN STRESS DETECTION BASED ON SLEEPING HABITS THROUGH MACHINE LEARNING ALGORITHMS

Stress is a mental or emotional state brought on by demanding or unavoidable circumstances, also referred to as stressors. In order to prevent any unfavorable occurrences in life, it is crucial to understand human stress levels. Sleep disturbances are related to a number of physical, mental, and social problems. This study's main objective is to investigate how human stress might be detected using machine learning algorithms based on sleep-related behaviors. Detecting and understanding stress patterns during sleep can provide valuable insights into individuals' overall stress levels and aid in the development of targeted interventions for stress management. This project focuses on the detection of human stress in and through sleep using various sensing modalities and machine learning techniques. The project employs a multimodal approach, combining physiological signals, sleep-related data, and contextual information to capture comprehensive stress patterns during sleep. Physiological signals such as heart rate, electro dermal activity, and respiratory patterns are collected using wearable sensors, while sleep-related data, including sleep stages and sleep quality metrics, are obtained through polysomnography and actigraphy. This data when given to the web application performs the prediction through the ML model and gives the predicted output. This project is made on a Decision Tree Classifier with best hyper parameters. Key Words: Stress, Sleep disturbances, Human stress, Physiological signals, Polysomnography, Actigraphy

Relative bioavailability of lutein and zeaxanthin in the presence of Omega-3- supplements and oxidative stress levels in humans

Lutein and zeaxanthin (LZ) are the major constituents of macular pigment (MP), helping to protect the human retina from blue light and oxidative damage(1). Many studies have suggested that higher concentrations of retina LZ may reduce the risk of age-related macular degeneration (AMD) and improve retinal health(1–3). MP and serum L have shown positive linear response with L dose(4) but the combined effect (LZ + omega-3 suppl) has not been fully explored in healthy Australian adults. Understanding their bioavailability in relation to the effect of omega-3 fatty acid intakes along with LZ supplements could provide a useful indication of the potential to reduce the risk of AMD, preserve vision, and improve retinal health. LZ uptake and the associated oxidative stress levels were evaluated in two groups fed with commercially sourced supplements. The control group was given only LZ, while the intervention group was given LZ combined with omega-3 supplements containing Eicosapentaenoic acid (EPA) and Docosahexaenoic acid (DHA). 10 men and 6 women with an average age of 31.4 ± 1.3yrs participated in this randomised, non-blinded controlled study for a total of 19-d. The control group (9) consumed the LZ supplement (12mg/d) only, while the intervention group (7) consumed the LZ supplement along with 900mg/d of an omega-3 supplement (540mg EPA + DHA 360mg). Each group adhered to a comprehensive low-carotenoid and omega-3 diet list for the 12-d intervention period and the 7-d washout period. Participants reported daily foods consumed in their diet logbooks, and Automated Self-Administered 24 diet assessment log over the study period. The body composition of each subject from the two groups was assessed before and after the study using a SECA body composition analyser and blood samples (2-time point) collected over a 12-d test period. Mean ± SEM for serum LZ ranged from 2.23 ± 0.24 – 2.98 ± 0.24µg/ml for the control group and 1.10 ± 0.21–3.02 ± 0.73µg/ml for the intervention group. Percentage change in serum LZ concentration from (T0-T312h) and (T312h-T456h) were 26% and 34% (control) and 139% and 175% for (intervention), respectively. The Area Under the Curve (AUC0-456h) differed significantly (P<0.0469) during the entire study period (between groups) and related to the cumulative effect of intakes at various times of blood draw. LZ from the intervention group was 68% more bioavailable than the control group. The highest peak relative response in subjects in the control group was ≈33% (a 9.1-fold increase from baseline) at AUC (168-312h) and ≈46% (a 6.6-fold increase from baseline) at AUC (312-456h) for the intervention group. No significant (p>0.05) effect of omega-3-supplement addition on oxidative stress levels was observed. Omega-3- addition to intakes of supplement LZ was responsible for the increased absorption (intervention) observed but did not affect oxidative stress levels and the Red Blood Cell omega-3-status.

Stress Detection Based on Human Sleep Cycle

Anxiety, mental or emotional stress resulting from a challenging environment, significantly impacts well-being. Understanding and monitoring human stress levels are crucial for averting adverse outcomes. This study investigates stress detection through machine learning and deep learning algorithms, focusing on sleep-related behaviours. Six machine learning techniques and deep learning methods, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Deeper Neural Networks, were employed and compared against benchmarks from prior studies. Notably, the Naïve Bayes algorithm exhibited exceptional performance, achieving 91.27% accuracy. The integration of deep learning methods provided a broader perspective on stress detection and complemented insights from established studies. Leveraging previous research results not only served as benchmarks for our model but also validated and extended our understanding of stress detection based on sleep-related behaviours. Our findings contribute to the discourse on human stress monitoring.

Advances in electrochemical biosensor design for the detection of the stress biomarker cortisol.

The monitoring of stress levels in humans has become increasingly relevant, given the recent incline of stress-related mental health disorders, lifestyle impacts, and chronic physiological diseases. Long-term exposure to stress can induce anxiety and depression, heart disease, and risky behaviors, such as drug and alcohol abuse. Biomarker molecules can be quantified in biological fluids to study human stress. Cortisol, specifically, is a hormone biomarker produced in the adrenal glands with biofluid concentrations that directly correlate to stress levels in humans. The rapid, real-time detection of cortisol is necessary for stress management and predicting the onset of psychological and physical ailments. Current methods, including mass spectrometry and immunoassays, are effective for sensitive cortisol quantification. However, these techniques provide only single measurements which pose challenges in the continuous monitoring of stress levels. Additionally, these analytical methods often require trained personnel to operate expensive instrumentation. Alternatively, low-cost electrochemical biosensors enable the real-time detection and continuous monitoring of cortisol levels while also providing adequate analytical figures of merit (e.g., sensitivity, selectivity, sensor response times, detection limits, and reproducibility) in a simple design platform. This review discusses the recent developments in electrochemical biosensor design for the detection of cortisol in human biofluids. Special emphasis is given to biosensor recognition elements, including antibodies, molecularly imprinted polymers (MIPs), and aptamers, as critical components of electrochemical biosensors for cortisol detection. Furthermore, the advantages and limiting factors of various electrochemical techniques and sensing in complex biofluid matrices are overviewed. Remarks on the current challenges and future perspectives regarding electrochemical biosensors for stress monitoring are provided, including matrix effects (pH dependence and biological interferences), wearability, and large-scale production.

A deep learning-based approach for distinguishing different stress levels of human brain using EEG and pulse rate

In today’s world, people suffer from many fatal maladies, and stress is one of them. Excessive stress can have deleterious effects on the health, brain, mind, and nervous system of humans. The goal of this paper is to design a deep learningbased human stress level measurement technique using electroencephalogram (EEG), and pulse rate. In this research, EEG signals and pulse rate of healthy subjects are recorded while they solve four different question sets of increasing complexity. It is assumed that the subjects undergo through four different stress levels, i.e., ‘no stress’, ‘low stress’, ‘medium stress’, and ‘high stress’, while solving these question sets. An attention mechanism-based CNN-TLSTM (convolutional neural network-tanh long short-term memory) model is proposed to detect the mental stress level of a person. An attention layer is incorporated into the designed TLSTM network to increase the classification accuracy of the CNN-TLSTM model. The CNN network is used for the automated extraction of intricate features from the EEG signals and pulse rate. Then TLSTM is used to classify the stress level of a person into four different categories using the CNNextracted features. The obtained average accuracy of the proposed CNN-TLSTM model is 97.86%. Experimentally, it is found that the designed stress level measurement technique is highly effective and outperforms most existing state-of-the-art techniques. In the future, functional Near-Infrared Spectroscopy (fNIRS), ECG, and Galvanic Skin Response (GSR) can be employed with EEG and pulse rate to increase the effectiveness of the designed stress level measurement technique.

Assessment of a Coordinated Arrangement of Wearable Physiological Sensors for Stress Checking in Work Spaces by Utilizing Organic Markers

The objectives of this investigation were to create and evaluate a wearable physiological sensor system for detecting human stress using ECG, EDA, and EEG data, as well as to see if the changes in physiological indicators were correlated levels. The main elements of the study are listed below. This study included 15 healthy volunteers with an average age of 40.8 years, comprising seven men and six women. They underwent the well-known Maastricht Intense Stress Test, a procedure that is known to cause significant physical and psychological stress, while wearing three commercial sensors have been employed to keep track of their physiologic responses.Throughout salivary samples were taken at various points. The use of a Salivary concentrations of cortisol were correlated with the obtained physiological indicators using an algorithm called the Support Vector Machine (SVM) segmentation technique were both part of the statistical analysis. A significant ability to discriminate between stress and relaxation was shown by fifteen features obtained from measurements including heart rate variability, electro dermal activity, and electroencephalography signals. Based on these important features, the classification system produced results with an accuracy rate of 86% that was satisfactory.Furthermore, the correlation analysis revealed, with an R-squared value of 0.714, a considerable consistency between changes in physiological characteristics and the trends in salivary cortisol levels. The study successfully showed that the wearable sensor system's implementation caught human stress levels and measured the state of stress with accuracy. The design of adaptable and controllable systems, such as medical devices, targeted at inducing interventions to stop stress-related effects, is particularly affected by these findings.

An electrochemical sensor based on FeCo bimetallic single-atom nanozyme for sensitive detection of H2O2

Efficient catalytic decomposition of H2O2 is accompanied by electron transfer through Fe-Nx active sites of hemin in the human body. Inspired by this reaction process, the Fe SAs/Co CNs were successfully synthesized by combined Co atomic sites with nitrogen-carbon doped Fe single-atom active sites (SAs). The synergy between transition metals not only reduces agglomeration during synthesis but also improves its own electrical conductivity due to the interaction between Fe and Co that promotes the formation of graphite surface. Crucially, the synergistic effect of the Co site significantly enhanced the peroxidase activity of the Fe SAs and the reaction rate of the Fenton-like reaction, resulting in an efficient detection of H2O2. Catalytic kinetic calculations and enzymatic kinetic calculations were used to verify the electron transfer rate and catalytic performance of their constructed electrochemical sensing interfaces. The results showed that Fe SAs/Co CNs@GCE showed better detection performance than Fe SAs CNs@GCE. It was applied successfully to detect H2O2 released from cells in real-time as well. The linear detection range of Fe SAs/Co CNs@GCE for H2O2 was 1–16664 μM, and the detection limit was as low as 0.25 μM. Furthermore, an electrochemical sensing chip was constructed using Fe SAs/Co CNs@SPE and the prepared microfluidic channel. The constructed portable FeSAs/Co CNs@SPE had a linear range of 1–400 μM and a detection limit of 0.36 μM and achieved the recovery detection of H2O2 in serum. The electrochemical sensing interfaces constructed based on Fe SAs/Co CNs all have efficient catalytic performance and excellent real-time hydrogen peroxide detection performance, which have practical application potential for human oxidative stress level detection. And it provides a novel approach to the trace detection of bioactive small molecules.

Real-time stress detection and monitoring system using IoT-based physiological signals

Currently, medical experts use psychophysiological questionnaires to evaluate human stress levels during counseling or interviews. Typically, biochemical samples use urine, saliva, and blood samples to identify the effects of stress on the human body. This research explains that stress detection can be done by analyzing psychological signals and the importance of monitoring stress levels. The authors develop research on stress detection based on psychological signals. The system then processes the recorded data; the android application displays the calculation results. The database can also be accessed as a spreadsheet via a web application. The design of real-time stress detection and monitoring using internet of things (IoT) can work well.

Penerapan Decision Tree dalam Mendeteksi Pola Tingkat Stress Manusia Berdasarkan Pola Tidur Menggunakan Rapid Miner

According to data from the Health Service Monitor in 2023, stress is one of the most worrying health problems for 30% of respondents. Stress is often associated with sleep patterns. This study aims to identify the relationship between sleep patterns and stress levels in humans using 10 levels: 1-2 (normal), 3-4 (mild), 5-6 (moderate), 7-8 (high), 9-10 (very high). The model used in this study is decision tree, with data covering gender, age, occupation, sleep quality, physical activity level, BMI (Body Mass Index) category, blood pressure, heart rate, daily activities, and sleep disorders. This study is expected to provide valuable information on the relationship between sleep patterns and stress, so that strategies can be developed to improve sleep quality and reduce stress. Based on the data analysis, there are several factors that cause increased stress levels, namely blood pressure, sleep quality, body weight, gender, and daily activities. This can have a significant impact on sleep quality.

Practical assessment strategy in human anatomy education, stress level and academic performance

Scientifical evidences indicates a correlation between alterations of chronical origin and the stress created in academicals activities. Among the obligatory disciplines in health-related university programs, practical evaluation in Human Anatomy is one of the most feared, which seems related to anxiety, naiveness and superstitions in handling corpses. Furthermore, the psychological pressure in traditional evaluation methods seems to act as an important stress factor. The objective of this study was to evaluate the correlation between two methods of practical assessment of Human Anatomy, continuous and intermittent flow, level of cortisol and overall performance in college students from health-related programs. Thirty two students were recruited. Samples were collected before and after the first and second practical evaluation. The students themselves collected the samples assisted by a previously trained professional using Salivette tubes. The first evaluation was applied individually in intermittent flow (IF), switching between a pratical question and a period waiting seated on a chair. The second was also individually, but in a continuous format (CF), without a stoppage. Levels of cortisol were determined by ELISA, utilizing the Salimetrics kit. The results point an increase of 46.8% in the level of stress of practical assessment strategy in the IF method. Nevertheless, a more expressive increase of 67.8% was observer in the CF method. In conclusion, IG method caused a smaller variation in cortisol levels and those have shown an inverse relation with student performance, indicating that the IG method may be a better way to apply practical evaluation in Human Anatomy Education.

Spatiotemporal changes in Universal Thermal Climate Index over South Asia

The rising global temperatures have posed challenges to human survival and well-being in various regions worldwide. However, little is known about human thermal stress exposure changes among populations living in extreme poverty and struggling to meet their basic needs. This study aimed to investigate the spatial patterns of changes in human thermal stress levels in South Asia using the Universal Thermal Climate Index (UTCI). ERA5 HEAT data from 1979 to 2021 were analyzed using Sen's slope method and the Modified Mann-Kendall test to determine the changes in annual and seasonal thermal stress levels. The findings revealed that Bangladesh, located in the eastern part of South Asia, experiences the highest mean UTCI range (26 to 32 °C), while Afghanistan has the lowest among the South Asian nations. In contrast, the western part of South Asia, particularly the region bordering India and Pakistan, exhibits the highest daily maximum UTCI range (38 to 46 °C). Trend analysis indicated an increase in mean and maximum UTCI levels across the western part of South Asia, including Pakistan, Afghanistan, and northwest India, at 0.25 to 0.75 °C/decade. This increase was more widespread during the June–September (JAS) period than in other seasons. In many regions of the study area, moderate and high thermal stress days increased by 4 to 8 days per decade. As a result, 27.6% of the area shifted from an average comfortable temperature to moderate thermal stress and 18.7% from moderate to strong thermal stress during JAS.

Obesity-induced oxidative stress and mitochondrial dysfunction negatively affect sperm quality.

Obesity is a systemic metabolic disease that can induce male infertility or subfertility through oxidative stress. The aim of this study was to determine how obesity impairs sperm mitochondrial structural integrity and function, and reduces sperm quality in both overweight/obese men and mice on a high-fat diet (HFD). Mice fed the HFD demonstrated higher body weight and increased abdominal fat content than those fed the control diet. Such effects accompanied the decline in antioxidant enzymes, such as glutathione peroxidase (GPX) and catalase and superoxide dismutase (SOD) in testicular and epidydimal tissues. Moreover, malondialdehyde (MDA) content significantly increased in sera. Mature sperm in HFD mice demonstrated higher oxidative stress, including increased mitochondrial reactive oxygen species (ROS) levels and decreased protein expression of GPX1, which may impair mitochondrial structural integrity and reduce mitochondrial membrane potential (MMP) and ATP production. Moreover, cyclic AMPK phosphorylation status increased, whereas sperm motility declined in the HFD mice. Clinical studies demonstrated that being overweight/obese reduced SOD enzyme activity in the seminal plasma and increased ROS in sperm, accompanied by lower MMP and low-quality sperm. Furthermore, ATP content in the sperm was negatively correlated with increases in the BMI of all clinical subjects. In conclusion, our results suggest that excessive fat intake had similar disruptive effects on sperm mitochondrial structure and function, as well as oxidative stress levels in humans and mice, which in turn induced lower sperm motility. This agreement strengthens the notion that fat-induced increases in ROS and impaired mitochondrial function contribute to male subfertility.

MicroSweat: A Wearable Microfluidic Patch for Noninvasive and Reliable Sweat Collection Enables Human Stress Monitoring (Adv. Sci. 7/2023)

MicroSweat for Human Stress Monitoring A wearable, self-power, skin-interfaced, and scalable surfactant-free microfluidic patch (MicroSweat) was developed and successfully used for reliable sweat collection and Chrono sampling of the human sweat. MicroSweat facilitates measuring the sweat cortisol stress biomarker in different body locations, with the most predictive cortisol concentration detectable in the armpits. This study provides valuable sweat cortisol data as an essential basis for the detection of human stress levels. More details can be found in article number 2204171 by Jaideep S. Bains, Amir Sanati Nezhad, and co-workers.

Fluorescence Masking Based Multifunctional Quantum Dots’ Assay for HSP90α Interactions Detection

HSP90α is one of the most common stress proteins in cells; hence, it is a good target for developing drugs and testing systems for cancer or physical stress levels in humans. Streptavidin conjugated quantum dots (Sav-QDs) are widely used as fluorophores for biosensing to overcome chemical labelling problems. In this work, we have attempted to develop a multifunctional and robust assay for HSP90α. The detection technique was based on the masking of the fluorescence of spotted Sav-QDs on nitrocellulose chips (NC). Biotinylated ligand/antibody attaches to the spotted Sav-QD and then HSP90α is attached, which causes the masking of fluorescence. The masking of fluorescence was used to detect protein–ligand interactions, the effect of inhibitors, protein–protein interactions, and the presence of protein in the biological sample. The load of detection (LoD) of the assay lies in the nano molar range, making it a sensitive assay. The results from the experiments suggest that the used approach is promising for developing a multifunctional, robust, and sensitive assay for proteins that can be used for point-of-care detection in complex biological samples.

Spatiotemporal changes in Hourly Wet Bulb Globe temperature in Peninsular Malaysia

Global warming causes a temperature rise and alteration of other meteorological variables that directly or indirectly affect human comfort. The wet bulb globe temperature (WBGT) incorporates the effects of multiple meteorological variables to provide a reliable measure of human thermal stress. Despite the large significance of WBGT on public health, studies related to characterization and trends assessment of WBGT are limited in the tropical humid region like Peninsular Malaysia due to the unavailability of all meteorological variables required for such analysis. This study employed reanalysis meteorological data of ERA5 to assess the characteristics and changes in hourly, daily, monthly, seasonal and annual outdoor WBGT over peninsular Malaysia for the period 1959–2021 using the Liljegren method. The WBGT values were classified into five categories to assess the human thermal stress levels defined by the United States Department of the Army (USDA). The mean daily WBGT in PM varies from 21.5 °C in the central south elevated region to 30.5 °C in the western coastal region. It always reaches a heat-related illness risk level (31.20 °C) in the afternoon during monsoon and extreme stress conditions during inter-monsoonal periods. The trend analysis revealed an increase in WBGT for all the time scales. The higher increase in the mean and maximum WBGT was estimated in the coastal and south regions, nearly by 0.10 to 0.25 °C/decade. The increase in mean nighttime WBGT was 0.24 °C/decade, while in mean daytime WBGT was 0.11 °C/decade. The increase in WBGT caused a gradual expansion of areas experiencing daily WBGT exceeding a high-risk level for 5 h (11 AM to 3 PM). The information and maps generated in this study can be used for mitigation planning of heat-related stress risk in PM, where temperature extremes have grown rapidly in recent years.

Wearable Localized Surface Plasmon Resonance-Based Biosensor with Highly Sensitive and Direct Detection of Cortisol in Human Sweat

Wearable biosensors have the potential for developing individualized health evaluation and detection systems owing to their ability to provide continuous real-time physiological data. Among various wearable biosensors, localized surface plasmon resonance (LSPR)-based wearable sensors can be versatile in various practical applications owing to their sensitive interactions with specific analytes. Understanding and analyzing endocrine responses to stress is particularly crucial for evaluating human performance, diagnosing stress-related diseases, and monitoring mental health, as stress takes a serious toll on physiological health and psychological well-being. Cortisol is an essential biomarker of stress because of the close relationship between cortisol concentration in the human body and stress level. In this study, a flexible LSPR biosensor was manufactured to detect cortisol levels in the human body by depositing gold nanoparticle (AuNP) layers on a 3-aminopropyltriethoxysilane (APTES)-functionalized poly (dimethylsiloxane) (PDMS) substrate. Subsequently, an aptamer was immobilized on the surface of the LSPR substrate, enabling highly sensitive and selective cortisol capture owing to its specific cortisol recognition. The biosensor exhibited excellent detection ability in cortisol solutions of various concentrations ranging from 0.1 to 1000 nM with a detection limit of 0.1 nM. The flexible LSPR biosensor also demonstrated good stability under various mechanical deformations. Furthermore, the cortisol levels of the flexible LSPR biosensor were also measured in the human epidermis before and after exercise as well as in the morning and afternoon. Our biosensors, which combine easily manufactured flexible sensors with sensitive cortisol-detecting molecules to measure human stress levels, could be versatile candidates for human-friendly products.

Alat Akuisisi Data 5 Parameter Sinyal Fisiologis Sebagai Penciri Stress Pada Manusia Berbasis Arduino MEGA

Stress is a non-specific response of the body to any demands on the body. It refers to events that are felt to be harmful to a person's physical and psychological well-being. One of the effects of stress on humans is depression. Depression that is allowed to continue can cause a decrease in work productivity, impaired social relationships to suicidal ideation. Increased stress due to lifestyle and current conditions has prompted many studies to be able to perform early detection of stress by measuring physiological signals. This study makes a tool to acquire data from 5 physiological signals of the body to later be used as variables for analyzing stress levels in humans. This tool uses an MPX5050DP sensor for blood pressure, a pulse sensor for heart rate detection, a DS18b20 sensor for body temperature, a sound sensor to calculate respiration frequency and a GSR sensor to measure the resistance value of human skin. The sensor will take data from the measured physiological parameters which will then be displayed on the LCD screen. The test was carried out on 5 test subjects with each subject taking data 5 times. The error rate of each parameter is obtained as follows: heart rate sensor 1.14%, temperature sensor 0.36%, blood pressure sensor 11.2% for systolic measurement and 4.9% for diastolic measurement, and respiration sensor 22.08 %.