Stress level measurements using heart rate variability analysis on android based application

Muhajir, Daud; Mahananto, Faizal; Sani, Nisfu Asrul

doi:10.1016/j.procs.2021.12.200

Cited by 9 publications

(8 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Future studies of women truck drivers must have larger sample sizes to be more powerful. Also, supplementing self-reported data with biologic measures such as hormone levels and heart rate variability will give much more comprehensive understanding of chronic stress response of the autonomic nervous system (Muhajir et al, 2022) and the hypothalamic-pituitary-adrenal (HPA) axis in women truck drivers. This is a critically important line of research with this population as dysregulation of either or both of these systems are key contributors to the development of cardiovascular disease risk.…”

Section: Limitations and Implications For Future Researchmentioning

confidence: 99%

Sleep, mental health, and access to health care of women truck drivers

Heaton,

Kham-Ai,

Shattell

2023

J Am Assoc Nurse Pract

View full text Add to dashboard Cite

Background: The numbers of women in trucking are growing steadily, yet because they represent a minority group, little is known about their health issues. Most studies of truck drivers have focused on the mental and physical health, sleep, and health care access of male truck drivers. Purpose: The purpose of this study was to characterize chronic stress, sleep, and mental health service and overall access to care among women truck drivers. Methodology: Twenty-five female truck drivers were a subsample of participants from a larger parent study of truck drivers. After approval from the institutional review board, participants completed a 59-item Qualtrics survey; data were transferred from Qualtrics to SPSS v. 24 for analysis. Results: No acute sleepiness or excess daytime sleepiness was observed, but participants only slept 6 hr per night, and all experienced poor sleep quality. Although 28% of participants met or exceeded the threshold score for posttraumatic stress disorder (PTSD), only 8% sought care for feelings of upset or distress. Also, 80% of the women had health insurance, yet there were those who did not seek care because of job-related conflicts. Conclusion/Implications: Participants were sleep deprived and experienced poor-quality sleep. Mental health and other health services utilization was low. Implications for practice include consideration of telehealth services to improve health care access and screening and referral as needed to mental health care providers by Department of Transportation medical examiners. Future research should include younger women truck drivers to determine the potential contribution of perimenopause/menopause to some of the health issues experienced by this group of workers.

show abstract

Section: Limitations and Implications For Future Researchmentioning

confidence: 99%

Sleep, mental health, and access to health care of women truck drivers

Heaton,

Kham-Ai,

Shattell

2023

J Am Assoc Nurse Pract

View full text Add to dashboard Cite

show abstract

“…Muhajir et al 26 performed stress level classification based on HRV datasets and achieved 70% accuracy on linear analysis and 60% accuracy on non-linear analysis, based on the sixteen HRV features. Giannakakis et al 27 used sixteen features to classify HRV dataset with an SVM classifier with 10-fold cross-validation in a stress recognition system.…”

Section: Related Workmentioning

confidence: 99%

Meta-Heuristic Feature Optimization for Predictive Analysis on HRV Dataset and Semantic Knowledge Representation for Stress Management: A Case-Study Towards Ethical AI

Chatterjee,

Ganesh,

Reigler

et al. 2023

Preprint

View full text Add to dashboard Cite

Background: Heart Rate Variability (HRV) is intimately associated with stress and can serve as a valuable indicator of the individual’s stress level. HRV is the variation in the length of time between heartbeats. Lower HRV is associated with higher stress levels, while higher HRV indicates better stress resilience and adaptability. The HRV parameters can be classified as − time-domain, frequency-domain, and non-linear. Parameters associated with HRV can be employed to assess individuals’ health and observe the effects of interventions such as exercise, stress reduction, and medication. Research in the field of artificial intelligence (AI) is ongoing that attempts to classify stress based on HRV data. HRV, which is associated with stress and physiological health, has received attention as a potential component to incorporate into models that classify and predict stress levels accurately. Monitoring HRV can offer insights into the interplay between stress and mental health, aiding in early detection and holistic approaches to well-being. Objective: The primary goal of this study is to perform semantic modeling of the vital HRV features in a knowledge graph, followed by, developing an accurate, reliable, explainable, and ethical AI model pipeline that can perform predictive analysis on HRV data. Methods: In this regard, we have considered the well-known multimodal SWELL knowledge work (SWELL−KW) dataset as a study case that represents the following stress conditions − no stress, time pressure, and interruption. The selected HRV dataset shows a labeled relationship between HRV and stress levels, which is deemed suitable for this study. We have explored different feature selection and dimensionality reduction techniques to extract relevant features from the HRV dataset to enhance classification accuracy with reduced bias. We have used various machine learning (ML) algorithms (e.g., traditional and ensemble) for the predictive analysis of imbalanced and balanced HRV datasets. We have used different data formats (e.g., scaled, normalized, and standardized) and various oversampling techniques (e.g., Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN)) to generate synthetic samples from the minority class. We have used a Tree-Explainer (e.g., Shapley Additive Explanations (SHAP)) to explain the model classifications. Results: As the HRV features are non-linear, the genetic algorithm-based feature selection followed by, model classification with Random Forest Classifier has produced the highest classification result on both the imbalanced and balanced datasets. The optimized feature set has been beneficial to design and develop a stress management system with a Semantic framework. Therefore, we have introduced the concept of domain ontology to represent the data and obtained knowledge. The consistency of the Ontology model has been evaluated with Hermit reasoners and reasoning time. Conclusions: Overall, HRV serves as a valuable physiological marker that can provide insights into the relationship between stress and mental health. It’s crucial to recognize that while HRV is a valuable non-invasive indicator of stress levels, its interpretation should be combined with other subjective and objective measures of stress in order to comprehensively understand the individual’s stress response. Therefore, the monitoring of HRV may help individuals assess the effectiveness of stress management techniques and interventions.

show abstract

“…Stress levels are often measured using subjective methods to extract perceptions of stress. Stress level measurement based on collected heart rate viability (HRV) data can help to remove the presence of stress by observing its effects on the autonomic nervous system (ANS) [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study

Mortensen,

Mollov,

Chatterjee

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Stress is a natural human reaction to demands or pressure, usually when perceived as harmful or/and toxic. When stress becomes constantly overwhelmed and prolonged, it increases the risk of mental health and physiological uneasiness. Furthermore, chronic stress raises the likelihood of mental health plagues such as anxiety, depression, and sleep disorder. Although measuring stress using physiological parameters such as heart rate variability (HRV) is a common approach, how to achieve ultra-high accuracy based on HRV measurements remains as a challenging task. HRV is not equivalent to heart rate. While heart rate is the average value of heartbeats per minute, HRV represents the variation of the time interval between successive heartbeats. The HRV measurements are related to the variance of RR intervals which stand for the time between successive R peaks. In this study, we investigate the role of HRV features as stress detection bio-markers and develop a machine learning-based model for multi-class stress detection. More specifically, a convolution neural network (CNN) based model is developed to detect multi-class stress, namely, no stress, interruption stress, and time pressure stress, based on both time-and frequency-domain features of HRV. Validated through a publicly available dataset, SWELL−KW, the achieved accuracy score of our model has reached 99.9% (Precision = 1, Recall = 1, F1−score = 1, and MCC = 0.99), thus outperforming the existing methods in the literature. In addition, this study demonstrates the effectiveness of essential HRV features for stress detection using a feature extraction technique, i.e., analysis of variance.

show abstract

Stress level measurements using heart rate variability analysis on android based application

Cited by 9 publications

References 5 publications

Sleep, mental health, and access to health care of women truck drivers

Sleep, mental health, and access to health care of women truck drivers

Meta-Heuristic Feature Optimization for Predictive Analysis on HRV Dataset and Semantic Knowledge Representation for Stress Management: A Case-Study Towards Ethical AI

Multi-Class Stress Detection Through Heart Rate Variability: A Deep Neural Network Based Study

Contact Info

Product

Resources

About