scholarly journals Continuous Stress Detection of Hospital Staff Using Smartwatch Sensors and Classifier Ensemble

2021 ◽  
Author(s):  
Muhammad Ali Fauzi ◽  
Bian Yang
Keyword(s):  
Stress Level ◽  
High Stress ◽  
Hospital Staff ◽  
Wearable Devices ◽  
Classifier Ensembles ◽  
Stress Detection ◽  
Monitoring Task ◽  
The Ensemble Method ◽  
The Individual ◽  
Voting Strategy

High stress levels among hospital workers could be harmful to both workers and the institution. Enabling the workers to monitor their stress level has many advantages. Knowing their own stress level can help them to stay aware and feel more in control of their response to situations and know when it is time to relax or take some actions to treat it properly. This monitoring task can be enabled by using wearable devices to measure physiological responses related to stress. In this work, we propose a smartwatch sensors based continuous stress detection method using some individual classifiers and classifier ensembles. The experiment results show that all of the classifiers work quite well to detect stress with an accuracy of more than 70%. The results also show that the ensemble method obtained higher accuracy and F1-measure compared to all of the individual classifiers. The best accuracy was obtained by the ensemble with soft voting strategy (ES) with 87.10% while the hard voting strategy (EH) achieved the best F1-measure with 77.45%.

Occupational Stress Impact and the Risk of Burn-Out for Employees of the Healthcare Sector, Germany

2018 ◽  
Vol 9 (6) ◽  
pp. 477-483
Author(s):  
Sabine Abbasi ◽  
Keyword(s):  
Occupational Stress ◽  
Working Conditions ◽  
Stress Level ◽  
High Stress ◽  
Working Hours ◽  
Healthcare Sector ◽  
Health Report ◽  
Burn Out ◽  
The Individual ◽  
High Stress Level

In accordance with the German annual health report mental disorders of employees continue to grow. In particular the German employees of the healthcare sector have work challenges like demographic change, lack of specialists, etc. Thus it seems obvious that these employees suffer from risk of a high stress level. The present paper focuses primarily on quantitative analysis of the stress level of employees in the rural healthcare sector. This study analyses the mental and physical burdens. The results of this paper support the concept that employees of the healthcare sector experience a strong stress levels. The results also support the assumption that communication and company structure is influencing the individual stress level of these employees. Further results show that physical and mental comfort is strongly influenced by weekly working hours and they show there is an impact of working atmosphere and working conditions to mental and physical burdens.

scholarly journals Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices

2021 ◽  
Author(s):  
Van-Tu Ninh ◽  
Sinéad Smyth ◽  
Minh-Triet Tran ◽  
Cathal Gurrin
Keyword(s):  
High Resolution ◽  
Stress Level ◽  
Electrodermal Activity ◽  
Low Cost ◽  
Wearable Devices ◽  
Detection Accuracy ◽  
Stress Detection ◽  
Low Resolution ◽  
Detection Model ◽  
Training Stress

Identifying stress level can provide valuable data for mental health analytics as well as labels for annotation systems. Although much research has been conducted into stress detection models using heart rate variability at a higher cost of data collection, there is a lack of research on the potential of using low-resolution Electrodermal Activity (EDA) signals from consumer-grade wearable devices to identify stress patterns. In this paper, we concentrate on performing statistical analyses on the stress detection capability of two popular approaches of training stress detection models with stress-related biometric signals: user-dependent and user-independent models. Our research manages to show that user-dependent models are statistically more accurate for stress detection. In terms of effectiveness assessment, the balanced accuracy (BA) metric is employed to evaluate the capability of distinguishing stress and non-stress conditions of the models trained on either low-resolution or high-resolution Electrodermal Activity (EDA) signals. The results from the experiment show that training the model with (comparatively low-cost) low-resolution EDA signal does not affect the stress detection accuracy of the model significantly compared to using a high-resolution EDA signal. Our research results demonstrate the potential of attaching the user-dependent stress detection model trained on personal low-resolution EDA signal recorded to collect data in daily life to provide users with personal stress level insight and analysis.

Stress Level Detection Using Electroencephalography Signals

2020 ◽  
Vol 17 (9) ◽  
pp. 4223-4228
Author(s):  
K. V. Suma ◽  
D. Venkatesh ◽  
Arun Kumar ◽  
Manjula Suryabhatla ◽  
Tejaswini M. Gowda ◽  
...  
Keyword(s):  
Stress Level ◽  
Classification Accuracy ◽  
Crucial Role ◽  
High Stress ◽  
Professional Life ◽  
Eeg Signal ◽  
Alpha Beta ◽  
The Individual ◽  
The Impact

Stress is the pressure that is experienced by humans. The impact of stress depends upon the type of stress the individual is experiencing. A positive stress may lead to the individual to feel motivated while a negative stress may impact the individual’s professional life or relationships. In this work, the approach of detecting the level of individual stress through EEG signal is presented. The EEG signal consists of set of components like Alpha, Beta and Gamma, out of which the dominating component plays a crucial role in determining the stress level. Results shows that 93.33%, 83.33% and 90% of classification accuracy, 87.5%, 80% and 85.71% of sensitivity and 95.45%, 86.66% and 91.30% of specificity for low, medium and high stress respectively is obtained. This work can be used to analyze the region of brain that is contributing more towards the individual’s stress.

A brief Survey of Assessment models to Predict stress Level of Heavy Metals in Soils

2020 ◽  
Vol 13 ◽  
Author(s):  
Sangeetha Annam ◽  
Anshu Singla
Keyword(s):  
Economic Growth ◽  
Heavy Metals ◽  
Heavy Metal ◽  
Ecological Risk ◽  
Stress Level ◽  
Metal Contamination ◽  
Heavy Metal Contamination ◽  
Human Life ◽  
High Stress ◽  
Heavy Metal Stress

Abstract: Soil is a major and important natural resource, which not only supports human life but also furnish commodities for ecological and economic growth. Ecological risk has posed a serious threat to the ecosystem by the degradation of soil. The high-stress level of heavy metals like chromium, copper, cadmium, etc. produce ecological risks which include: decrease in the fertility of the soil; reduction in crop yield & degradation of metabolism of living beings, and hence ecological health. The ecological risk associated, demands the assessment of heavy metal stress levels in soils. As the rate of stress level of heavy metals is exponentially increasing in recent times, it is apparent to assess or predict heavy metal contamination in soil. The assessment will help the concerned authorities to take corrective as well as preventive measures to enhance the ecological and hence economic growth. This study reviews the efficient assessment models to predict soil heavy metal contamination.

scholarly journals Stress level effect on mobility of dry granular flows of angular rock fragments

Landslides ◽  
2021 ◽  
Author(s):  
B. Cagnoli
Keyword(s):  
Stress Level ◽  
Granular Flow ◽  
High Stress ◽  
Granular Flows ◽  
Flow Dynamics ◽  
Small Scale ◽  
Flow Volume ◽  
Rock Fragments ◽  
Rock Avalanches ◽  
Flow Mobility

AbstractGranular flows of angular rock fragments such as rock avalanches and dense pyroclastic flows are simulated numerically by means of the discrete element method. Since large-scale flows generate stresses that are larger than those generated by small-scale flows, the purpose of these simulations is to understand the effect that the stress level has on flow mobility. The results show that granular flows that slide en mass have a flow mobility that is not influenced by the stress level. On the contrary, the stress level governs flow mobility when granular flow dynamics is affected by clast agitation and collisions. This second case occurs on a relatively rougher subsurface where an increase of the stress level causes an increase of flow mobility. The results show also that as the stress level increases, the effect that an increase of flow volume has on flow mobility switches sign from causing a decrease of mobility at low stress level to causing an increase of mobility at high stress level. This latter volume effect corresponds to the famous Heim’s mobility increase with the increase of the volume of large rock avalanches detected so far only in the field and for this reason considered inexplicable without resorting to extraordinary mechanisms. Granular flow dynamics is described in terms of dimensionless scaling parameters in three different granular flow regimes. This paper illustrates for each regime the functional relationship of flow mobility with stress level, flow volume, grain size, channel width, and basal friction.

scholarly journals Personal Stress-Level Clustering and Decision-Level Smoothing to Enhance the Performance of Ambulatory Stress Detection With Smartwatches

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 38146-38163 ◽  
Author(s):  
Yekta Said Can ◽  
Niaz Chalabianloo ◽  
Deniz Ekiz ◽  
Javier Fernandez-Alvarez ◽  
Giuseppe Riva ◽  
...  
Keyword(s):  
Stress Level ◽  
Stress Detection ◽  
Decision Level ◽  
Personal Stress

scholarly journals Crowd behaviour during high-stress evacuations in an immersive virtual environment

2016 ◽  
Vol 13 (122) ◽  
pp. 20160414 ◽  
Author(s):  
Mehdi Moussaïd ◽  
Mubbasir Kapadia ◽  
Tyler Thrash ◽  
Robert W. Sumner ◽  
Markus Gross ◽  
...  
Keyword(s):  
Virtual Environments ◽  
Virtual Environment ◽  
Human Subjects ◽  
High Stress ◽  
Three Dimensional ◽  
Real Life ◽  
Social Conventions ◽  
Emergency Situations ◽  
Crowd Behaviour ◽  
The Individual

Understanding the collective dynamics of crowd movements during stressful emergency situations is central to reducing the risk of deadly crowd disasters. Yet, their systematic experimental study remains a challenging open problem due to ethical and methodological constraints. In this paper, we demonstrate the viability of shared three-dimensional virtual environments as an experimental platform for conducting crowd experiments with real people. In particular, we show that crowds of real human subjects moving and interacting in an immersive three-dimensional virtual environment exhibit typical patterns of real crowds as observed in real-life crowded situations. These include the manifestation of social conventions and the emergence of self-organized patterns during egress scenarios. High-stress evacuation experiments conducted in this virtual environment reveal movements characterized by mass herding and dangerous overcrowding as they occur in crowd disasters. We describe the behavioural mechanisms at play under such extreme conditions and identify critical zones where overcrowding may occur. Furthermore, we show that herding spontaneously emerges from a density effect without the need to assume an increase of the individual tendency to imitate peers. Our experiments reveal the promise of immersive virtual environments as an ethical, cost-efficient, yet accurate platform for exploring crowd behaviour in high-risk situations with real human subjects.

scholarly journals Researches on the Constitutive Models of Artificial Frozen Silt in Underground Engineering

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yugui Yang ◽  
Feng Gao ◽  
Hongmei Cheng ◽  
Yuanming Lai ◽  
Xiangxiang Zhang
Keyword(s):  
Mechanical Characteristic ◽  
Stress Level ◽  
Strain Softening ◽  
Triaxial Compression ◽  
High Stress ◽  
Constitutive Models ◽  
Confining Pressure ◽  
Frozen Soil ◽  
Conventional Triaxial Compression ◽  
Shear Process

The researches on the mechanical characteristic and constitutive models of frozen soil have important meanings in structural design of deep frozen soil wall. In the present study, the triaxial compression and creep tests have been carried out, and the mechanical characteristic of frozen silt is obtained. The experiment results show that the deformation characteristic of frozen silt is related to confining pressure under conventional triaxial compression condition. The frozen silt presents strain softening in shear process; with increase of confining pressure, the strain softening characteristic gradually decreases. The creep curves of frozen silt present the decaying and the stable creep stages under low stress level; however, under high stress level, once the strain increases to a critical value, the creep strain velocity gradually increases and the specimen quickly happens to destroy. To reproduce the deformation behavior, the disturbed state elastoplastic and new creep constitutive models of frozen silt are developed. The comparisons between experimental results and calculated results from constitutive models show that the proposed constitutive models could describe the conventional triaxial compression and creep deformation behaviors of frozen silt.

scholarly journals Efficacy of exposure in group settings for youth with posttraumatic stress symptoms

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Elisa Pfeiffer
Keyword(s):  
Stress Level ◽  
Group Intervention ◽  
Behavioral Treatment ◽  
Traumatic Experiences ◽  
Cognitive Behavioral Treatment ◽  
Group Settings ◽  
Stress Symptoms ◽  
Group Sessions ◽  
Exposure Sessions ◽  
The Individual

Abstract Background Exposure to traumatic experiences is a fundamental part of evidence-based trauma-focused cognitive behavioral treatment (CBT) but in group settings it is discussed controversially among researchers and practitioners. This study aims to examine the individual participants’ stress level during group sessions with exposure and disclosure of traumatic events. Method N = 47 traumatized youth (Mage = 17.00, 94% male) participated in a group intervention comprising six 90-min group sessions (exposure in sessions 2–5). It is based on trauma-focused CBT principles. The individual stress level was assessed by the participants and group facilitators at the beginning, during, and at the end of every session. Results During the sessions including exposure, the stress level of the participants was higher than during sessions without exposure (Z = − 3.79; p ≤ .001). During the exposure sessions, the participants showed significant changes in stress level (d = 0.34–0.87) following an inverse U-shaped trend. Conclusion The results show that exposure is feasible within the scope of a trauma-focused group intervention for youth. The further dissemination of trauma-focused group treatments is an important component in the mental health care of children and youth who are traumatized.

scholarly journals Survey on Stress Detection Using Multiple Sensors through Wearable Devices

2021 ◽  
Vol 10 (2) ◽  
pp. 787-790
Keyword(s):  
Machine Learning ◽  
Wearable Devices ◽  
Anxiety And Depression ◽  
Accuracy Score ◽  
Machine Learning Method ◽  
Stress Detection ◽  
Multiple Sensors ◽  
Machine Learning Model ◽  
Wide Range ◽  
Principle Objective

An Individual method of living on with a daily existence it directly influences on your overall health. Since stress is the significant infection of our human body. Like depression, heart attack and mental illness. WHO says “Globally, more than 264 million people of all ages suffer from depression.”[8]. Also the report says that most of the time people are stressed because of their work. 10.7% of People disorder with stress, anxiety and depression [8]. There are different method to discovering stress ex. Smart watches, chest belt, and extraordinary machine. Our principle objective is to figure out pressure progressively utilizing smart watches through their Sensor. There are different kinds of sensor available to find stress such as PPG, GSR, HRV, ECG and temperature. Smart watches contain a wide range of data through various sensor. This kind of gathered information are applied on various machine learning method. Like linear regression, SVM, KNN, decision tree. Technique have distinct, comparing accuracy and chooses best Machine learning model. This paper investigation have different analysis to find and compare accuracy by various sensors data. It is also check whether using one sensor or multiple sensors such as HRV, ECG or GSR and PPG to predict the better accuracy score for stress detection.

Sign in / Sign up

Export Citation Format

Share Document