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3 results on '"survey stress"'

1. Biosignals, facial expressions, and speech as measures of workplace stress: Workstress3d dataset

Author

Gulin Dogan, Fatma Patlar Akbulut, and Cagatay Catal

Subjects
Physiological signals, Face expression, Audio signal, Survey Stress, Wearable device, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

WorkStress3D is a comprehensive collection of multimodal data for the research of stress in the workplace. This dataset contains biosignals, facial expressions, and speech signals, making it an invaluable resource for stress analysis and related studies. The ecological validity of the dataset was ensured by the fact that the data were collected in actual workplace environments. The biosignal data contains measurements of electrodermal activity, blood volume pressure, and cutaneous temperature, among others. High-resolution video recordings were used to capture facial expressions, allowing for a comprehensive analysis of facial cues associated with tension. In order to capture vocal characteristics indicative of tension, speech signals were recorded. The dataset contains samples from both stress-free and stressful work situations, providing a proportionate representation of various stress levels. The dataset is accompanied by extensive metadata and annotations, which facilitate in-depth analysis and interpretation. WorkStress3D is a valuable resource for developing and evaluating stress detection models, examining the impact of work environments on stress levels, and exploring the potential of multimodal data fusion for stress analysis.

Published
2024
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2. Biosignals, facial expressions, and speech as measures of workplace stress: Workstress3d dataset.

Author

Dogan G, Akbulut FP, and Catal C

Abstract

WorkStress3D is a comprehensive collection of multimodal data for the research of stress in the workplace. This dataset contains biosignals, facial expressions, and speech signals, making it an invaluable resource for stress analysis and related studies. The ecological validity of the dataset was ensured by the fact that the data were collected in actual workplace environments. The biosignal data contains measurements of electrodermal activity, blood volume pressure, and cutaneous temperature, among others. High-resolution video recordings were used to capture facial expressions, allowing for a comprehensive analysis of facial cues associated with tension. In order to capture vocal characteristics indicative of tension, speech signals were recorded. The dataset contains samples from both stress-free and stressful work situations, providing a proportionate representation of various stress levels. The dataset is accompanied by extensive metadata and annotations, which facilitate in-depth analysis and interpretation. WorkStress3D is a valuable resource for developing and evaluating stress detection models, examining the impact of work environments on stress levels, and exploring the potential of multimodal data fusion for stress analysis., (© 2024 The Author(s).)

Published
2024
Full Text
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3. Voice perturbations under the stress overload in young individuals: Phenotyping and suboptimal health as predictors for cascading pathologies

Author

Holger Fröhlich, Anatoly A Kunin, Natalia S. Moiseeva, N. Sargheini, Olga Golubnitschaja, Colin Birkenbihl, and Publica

Subjects
Hyposalivation, Disease, Overweight, Bioinformatics, Sicca syndrome, Tinnitus, underweight, Drug Discovery, risk factors, Individualised patient profile, machine learning models, dry mouth syndrome, education.field_of_study, Health Policy, Otorhinolaryngologoical disorders, Healthcare, risk assessment, Vasospasm, biomarker pattern, flammer syndrome, thirst, population screening, Biomarker (medicine), medicine.symptom, circadian rhythm, phenotyping, sense regulation, Population, predictive preventive personalised medicine, microcirculation, respiratory complications, body mass index, artificial intelligence (AI), Xerostomia, Flammer syndrome, suboptimal health, voice perturbation, Diabetes mellitus, high altitude sickness, survey stress, medicine, education, Biochemistry, medical, Stress, survey, business.industry, Research, pandemic, Biochemistry (medical), disease predisposition, association, COVID-19, lifestyle intervention, medicine.disease, exercise-induced hypoalgesia, primary vascular dysregulation, pain sensitivity, business, Body mass index
Abstract

Verbal communication is one of the most sophisticated human motor skills reflecting both—the mental and physical health of an individual. Voice parameters and quality changes are usually secondary towards functional and/or structural laryngological alterations under specific systemic processes, syndrome and pathologies. These include but are not restricted to dry mouth and Sicca syndromes, body dehydration, hormonal alterations linked to pubertal, menopausal, and andropausal status, respiratory disorders, gastrointestinal reflux, autoimmune diseases, endocrinologic disorders, underweight versus overweight and obesity, and diabetes mellitus. On the other hand, it is well-established that stress overload is a significant risk factor of cascading pathologies, including but not restricted to neurodegenerative and psychiatric disorders, diabetes mellitus, cardiovascular disease, stroke, and cancers. Our current study revealed voice perturbations under the stress overload as a potentially useful biomarker to identify individuals in suboptimal health conditions who might be strongly predisposed to associated pathologies. Contextually, extended surveys applied in the population might be useful to identify, for example, persons at high risk for respiratory complications under pandemic conditions such as COVID-19. Symptoms of dry mouth syndrome, disturbed microcirculation, altered sense regulation, shifted circadian rhythm, and low BMI were positively associated with voice perturbations under the stress overload. Their functional interrelationships and relevance for cascading associated pathologies are presented in the article. Automated analysis of voice recordings via artificial intelligence (AI) has a potential to derive digital biomarkers. Further, predictive machine learning models should be developed that allows for detecting a suboptimal health condition based on voice recordings, ideally in an automated manner using derived digital biomarkers. Follow-up stratification and monitoring of individuals in suboptimal health conditions are recommended using disease-specific cell-free nucleic acids (ccfDNA, ctDNA, mtDNA, miRNA) combined with metabolic patterns detected in body fluids. Application of the cost-effective targeted prevention within the phase of reversible health damage is recommended based on the individualised patient profiling.

Published
2020

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