Psychological Stress Level Detection Based on Heartbeat Mode

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(3), P. 1409 - 1409

Published: Jan. 28, 2022

The effective detection and quantification of mental health has always been an important research topic. Heart rate variability (HRV) analysis is a useful tool for detecting psychological stress levels. However, there no consensus on the optimal HRV metrics in assessments. This study proposes method that based heartbeat modes to detect drivers’ stress. We used statistical tools linguistics quantify structure heart time series summarized different series. Based k-nearest neighbors (k-NN) classification algorithm, probability each mode was as feature recognize caused by driving environment. results indicated from environment changed mode. Stress-related were determined, facilitating state with accuracy 93.7%. also concluded correlated galvanic skin response (GSR) signal, reflecting real-time abnormal mood fluctuations. proposed revealed characteristics made quantifying conditions possible. Thus, it would be feasible achieve personalized analyses further interaction between physiology psychology.

Language: Английский

---

Explainable stress type classification captures physiologically relevant responses in the Maastricht Acute Stress Test

Frontiers in Neuroergonomics, Journal Year: 2023, Volume and Issue: 4

Published: Dec. 5, 2023

Introduction Current stress detection methods concentrate on identification of and non-stress states despite the existence various types. The present study performs a more specific, explainable classification, which could provide valuable information physiological reactions. Methods Physiological responses were measured in Maastricht Acute Stress Test (MAST), comprising alternating trials cold pressor (inducing pain) mental arithmetics (eliciting cognitive social-evaluative stress). these subtasks compared to each other baseline through mixed model analysis. Subsequently, type was conducted with comprehensive analysis several machine learning components affecting classification. Finally, artificial intelligence (XAI) applied analyze influence features behavior. Results Most investigated reactions specific stressors, be distinguished from up 86.5% balanced accuracy. choice signals measure (up 25%-point difference accuracy) selection 7%-point difference) two key Reflection XAI results human physiology revealed that concentrated relevant for stressors. Discussion findings confirm multimodal classification can detect different types while focusing physiologically sensible changes. Since feature affected performance most, data analytic choices left limited input uncompensated.

Language: Английский

---

Issues and Challenges in Detecting Mental Stress from Multimodal Data Using Machine Intelligence

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(4)

Published: March 28, 2024

Language: Английский

---

LSTM‐based real‐time stress detection using PPG signals on raspberry Pi

IET Wireless Sensor Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 30, 2024

Abstract Stress, widely recognised for its profound adverse effects on both physical and mental health, necessitates the development of innovative real‐time detection methods. In this context, escalating prevalence wearable embedded systems, integrated with artificial intelligence (AI) continuous monitoring critical physiological indicators like heart rate blood pressure, accentuates their growing relevance in efficient stress. This article presents an methodology employing deep learning algorithms Raspberry Pi 3, a platform distinguished by cost‐effectiveness limited resources. The authors have developed advanced AI algorithm that achieves high accuracy stress using photoplethysmography (PPG) sensors while significantly reducing computational demands. authors’ method utilises neural network long short‐term memory (LSTM) layers, proving highly effective time‐series data analysis. study, implement key TensorFlow toolkit optimisation techniques including quantisation aware training (QAT), Pruning prune‐preserving training. These are applied to refine model, decreasing size latency without sacrificing accuracy. results highlight LSTM‐based model's proficiency accurately detecting raw PPG sensor comparatively affordable platform. model attains 89.32% F1 score 89.55% diverse affect stress‐level dataset. Additionally, optimised exhibits substantial reductions maintaining approach shows great potential various applications, such as healthcare, sports, workplace settings. use 3 makes system portable, cost‐effective, energy‐efficient, enhancing impact accessibility.

Language: Английский

---

Investigating How Auditory and Visual Stimuli Promote Recovery After Stress With Potential Applications for Workplace Stress and Burnout: Protocol for a Randomized Trial

Frontiers in Psychology, Journal Year: 2022, Volume and Issue: 13

Published: June 2, 2022

Background Work-related stress is one of the top sources amongst working adults. Relaxation rooms are organizational strategy being used to reduce workplace stress. Amongst healthcare workers, relaxation have been shown improve perceived levels after 15 min use. However, few studies examined physiological and cognitive changes stress, which may inform why Understanding biological mechanisms governing improves when using a room could lead more effective strategies address Objective The purpose this research study understand how measures, performance, change acute whether certain sensory features at promoting recovery from Methods 80 healthy adults will perform induction task (Trier Social Stress Test, TSST) evaluate responses affected by room. After task, participants recover for 40 in MindBreaks™ containing auditory visual stimuli designed promote relaxation. Participants be randomized into four cohorts experience stimuli; or no Measures heart rate neural activity continuously monitored wearable devices. memory assessments their throughout experiment. These measures compared before determine different affect individuals recover. Results Recruitment started December 2021 continue until 2022 enrollment completed. Final data collection subsequent analysis anticipated 2022. We expect all trial results available early 2023. Discussion Findings provide information about most This useful determining these might creating individualized mitigating effects

Language: Английский

---

Stress Detection using Wearable Physiological Sensors and Machine Learning Algorithm

2022 6th International Conference on Electronics, Communication and Aerospace Technology, Journal Year: 2022, Volume and Issue: unknown, P. 972 - 977

Published: Dec. 1, 2022

The primary objective of this methodology is to develop and test the performance a wearable psychological sensor using EEG, EDA ECG observe stress level humans analyze if observed variation in signals related biomarker stress. An integrated system with physiological sensors designed, developed, tested evaluated paper for monitoring biological markers working environment. Stress detection performed Muse S (Gen 2) EEG headset, Savvy sensor, Shimmer3 GSR sensors. For each subject under test, 32 features are extracted from multi-modal signals, which, five retained four 1 EDA, summing up 10 features. window size collection feature one minute. Hence, 20 participants ten features, data 200 minutes used as training controlled Of these, 138 labelled stressful tasks 62 labeled no-stress. proposed model effectively assesses environment an accuracy 97% everyday 93%. This work aims design remote control that can be medical devices prevent aftereffects

Language: Английский

---

Stress Monitoring in Humans using Biomedical Signal Analysis

Published: April 5, 2023

Nowadays, stress is one of the major issues in every individual's life and may cause both physiological psychological problems. Researchers have taken this into account proposed various detection models, which can be measured by using biomedical signals like Electrocardiogram (ECG), Electromyogram (EMG), Electroencephalogram (EEG) Electroneurogram (ENG). EEG are capable measuring monitoring neurological electrical activity detecting changes brain. The data gathered from PhysioNet pre-processed filtering out noise a fourth-order Butterworth filter decomposing it different frequency bands (y (>30Hz), β (13-30Hz), $a$ (S-13Hz), θ (4-8Hz), δ (0.5-4Hz)) Discrete Wavelet Transform (DWT) fifth-order Daubechies wavelet family. A variety signal characteristics were then extracted to examine patterns under circumstances. Support Vector Machine (SVM) classifier used study classify results aims improve accuracy efficiency compared existing models.

Language: Английский

---

Hierarchical Autoencoder Frequency Features for Stress Detection

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 103232 - 103241

Published: Jan. 1, 2023

Stress has a significant negative impact on people, which made it primary social concern. Early stress detection is essential for effective management. This study proposes Deep Learning (DL) method using multimodal physiological signals - Electrocardiogram (ECG) and Electrodermal activity (EDA). The extensive latent feature representation of DL models yet to be fully explored. Hence, this paper hierarchical autoencoder fusion the frequency domain. representations from different layers AutoEncoders(AE) are combined given as input classifier Convolutional Recurrent Neural Network with Squeeze Excitation (CRNN-SE) model. A two-set performance comparison performed (i) band features, raw data compared. (ii) autoencoders trained three cost functions Mean Squared Error (MSE), Kullback-Leibler (KL) divergence, Cosine similarity compared features data. To verify generalizability our approach, we tested four benchmark datasets- WAUC, CLAS, MAUS ASCERTAIN. Results show that showed better results than by 4-8%, respectively. MSE loss produced other losses both 3-7%, proposed approach considerably outperforms existing subject-independent 1–2%,

Language: Английский

---

Stress Detection Using Wearable Devices based on Transfer Learning

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Journal Year: 2021, Volume and Issue: unknown, P. 3122 - 3128

Published: Dec. 9, 2021

Excessive stress will have a negative impact on people's physical and mental health, especially for some special occupations. Because stressful stimuli can trigger variety of physiological responses, analyzing signals collected by wearable devices has become an important way to evaluate the state in recent years. However, number available subjects target group may be small, collecting large amount data when changes is costly time-consuming. To solve this problem, we propose detection framework small which uses adversarial transfer learning method learn shared knowledge about between different groups. In order verify performance framework, establish dataset consisting 264 ordinary college students 32 police school students, aiming acute under video psychological training future. Comprehensive experiments show that our algorithm achieved significant improvement compared with baseline methods.

Language: Английский

---

Investigating Human Physiological Responses to Work-Related Stress

Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Journal Year: 2023, Volume and Issue: 67(1), P. 2285 - 2290

Published: Sept. 1, 2023

Studies have shown that work-related stress is one of the causes employee burnout, fatigue, and cognitive dysfunction, among other negative effects. Physiological features been used to investigate stress, but more knowledge needed in understanding physiological indicators stress. Moreover, best our knowledge, no study available integrates both pupillometry heart rate investigating We, therefore, utilized task-evoked pupillary response (TEPR) from (HR), assessment responses 32 subjects during performance Multi-Attribute Task Battery-II consisting working baseline conditions. A comparison results conditions showed TEPR mean HR significantly increased condition, as compared condition. These are attributed work related-stressors integrated study, thereby bolstering applicability studies

Language: Английский

---