Evaluation of an Integrated System of Wearable Physiological Sensors for Stress Monitoring in Working Environments by Using Biological Markers (original) (raw)
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A Wearable System for Stress Detection Through Physiological Data Analysis
Lecture Notes in Electrical Engineering, 2017
In the last years the impact of stress on the society has been increased, resulting in 77% of people that regularly experiences physical symptoms caused by stress with a negative impact on their personal and professional life, especially in aging working population. This paper aims to demonstrate the feasibility of detection and monitoring of stress, inducted by mental stress tests, through the analysis of physiological data collected by wearable sensors. In fact, the physiological features extracted from heart rate variability and galvanic skin response showed significant differences between stressed and not stressed people. Starting from the physiological data, the work provides also a cluster analysis based on Principal Components (PCs) able to showed a visual discrimination of stressed and relaxed groups. The developed system would support active ageing, monitoring and managing the level of stress in ageing workers and allowing them to reduce the burden of stress related to the workload on the basis of personalized interventions.
Review of Stress Detection Methods Using Wearable Sensors
2024
Stress is a significant factor that affects well-being and health. Factors that trigger stress include work, social interactions, and economic and environmental factors. Stress may cause lower labor productivity, physical and mental health problems, and malfunctions in all social aspects of life. Psychosomatic health can be improved if proper stress detection mechanisms are present in daily life and stress reduction methods can occur. Wearable sensors are currently used in many commercial and scientific applications in a non-invasive or unobtrusive manner. These devices are used in daily routines. In this paper, a comprehensive review of the latest literature and developments in stress detection methods is presented through extensive and holistic research on stress response, both at the level of the autonomic nervous system (ANS) and hypothalamic-pituitary-adrenal axis (HPA). This review focuses on the exploitation of various methods, technologies, and data analysis systems to understand stress in a multifaceted and comprehensive manner. Various stress-related factors are presented along with biological signal measurements, and physical secretions or biomarkers are primarily used for stress detection. Furthermore, the manner in which body movement and posture measurements may be related to stress was investigated, together with speech and hand tremors. Various stress-detection technologies have been analyzed, and existing data analysis methods that can be applied to stress-detection systems have been highlighted. This review serves as a reference and guideline for exploring this area of interest, identifying research opportunities, and offering ideas, options, and suggestions for optimized solutions regarding future applications and research.
Lecture Notes in Electrical Engineering, 2019
Mental stress is a physiological state that directly correlates to the quality of life of individuals. Generally speaking, but especially true for disabled or elderly subjects, the assessment of such condition represents a very strong indicator correlated to the difficulties, and, in some case, to the frustration that derives from the execution of a task that results troublesome to be accomplished. This article describes a novel procedure for the assessment of the mental stress level through the use of low invasive wireless wearable devices. The information contained in electrocardiogram, respiratory signal, blood volume pulse, and electroencephalogram was extracted to set up an estimator for the cognitive workload level. A random forest classifier was implemented to assess the level of mental stress starting from a pool of 3481 features computed from the aforementioned physiological quantities. The proposed system was applied in a scenario in which two different mental states were elicited in the subject under investigation: first, a baseline resting condition was induced by the presentation of a relaxing video; then a stressful cognitive state was provoked by the
An Improved Approach for Stress Detection Using Physiological Signals
ICST Transactions on Scalable Information Systems
Stress is a major problem in society. Prolonged stress can lead to ill-health and a decrease in self-confidence. It is necessary to detect stress at an early stage to prevent its adverse effects on our physical and psychological health. The paper presents a stress detection model using physiological signals. In this paper, WESAD (Wearable and Stress Affect Detection) dataset is used which consists of physiological data recorded from both the chest and wrist. Further, a Long Short-Term Memory (LSTM) based model is used to detect stress. The simulation results indicate that, indeed, Electrocardiograph (ECG), Electromyogram (EMG), and Respiration (RESP) signals may not be necessary for identifying stress. A three-way validation is carried out with an accuracy of 98%. The novelty of the paper is the way time-series data is handled to make it closer to real-time data captured from sensors. The work can be used widely in clinical practices to detect stress at an early stage.
Stress Detection Using Multiple Bio-Signals
2014
Organizations are becoming more and more dependent on computers in their day to day activities. Employees spend hours daily interacting with various software that is needed to finish their work. This human computer interaction (HCI) may induce stress for various reasons such as bad user interface design, slow responses from the software, and much more. Stress will affect the employee's total performance and productivity, which will have a negative impact on their teams and organization. Our purpose is to have an integrated mechanism that will detect stress during HCI. The tool will be a starting point for providing a solution that aims to reduce stress in the HCI aspects at organizations. This study investigates the usage of two bio signals (EEG, ECG,) for the detection of stress during HCI.
The Concept of Advanced Multi-Sensor Monitoring of Human Stress
Sensors
Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respir...
Discriminating Stress From Cognitive Load Using a Wearable EDA Device
IEEE Transactions on Information Technology in Biomedicine, 2000
The inferred cost of work-related stress call for prevention strategies that aim at detecting early warning signs at the workplace. This paper goes one step towards the goal of developing a personal health system for detecting stress. We analyze the discriminative power of electrodermal activity (EDA) in distinguishing stress from cognitive load in an office environment. A collective of 33 subjects underwent a laboratory intervention that included mild cognitive load and two stress factors, which are relevant at the workplace: mental stress induced by solving arithmetic problems under time pressure and psychosocial stress induced by social-evaluative threat. During the experiments, a wearable device was used to monitor the EDA as a measure of the individual stress reaction. Analysis of the data showed that the distributions of the EDA peak height and the instantaneous peak rate carry information about the stress level of a person. Six classifiers were investigated regarding their ability to discriminate cognitive load from stress. A maximum accuracy of 82.8% was achieved for discriminating stress from cognitive load. This would allow keeping track of stressful phases during a working day by using a wearable EDA device.
Physiological Signals for Stress Measurements
2019
The aim of this paper is to give a brief overview about the physiological parameters and sensors that could be used to perform stress detection. Stress is considered one of the most serious social problem in today’s society, in particular work-related stress. An unhealthy level of stress is a direct cause of diseases and disorders, such as difficulty in concentration and decision, chronic fatigue, depression, emotional problems, anxiety, irritability and insomnia. Stress has a high social cost due to medical treatments, lost productivity and absenteeism. Hence the need to have accurate measurements of stress level to apply mechanisms for prevention and treatment. Keywords—stress measurement, physiological signals, wellbeing.
Biosensors
In the modern world, wearable smart devices are continuously used to monitor people’s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and no...