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Dogan G, Akbulut FP, Catal C. Biosignals, facial expressions, and speech as measures of workplace stress: Workstress3d dataset. Data Brief 2024; 54:110303. [PMID: 38559821 PMCID: PMC10981000 DOI: 10.1016/j.dib.2024.110303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 01/26/2024] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
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.
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Affiliation(s)
- Gulin Dogan
- Department of Computer Engineering, Istanbul Aydın University, Istanbul, Turkey
| | - Fatma Patlar Akbulut
- Department of Software Engineering, Istanbul Kültür University, Istanbul, Turkey
| | - Cagatay Catal
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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Derdiyok S, Akbulut FP, Catal C. Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset. Data Brief 2024; 52:109896. [PMID: 38173979 PMCID: PMC10762351 DOI: 10.1016/j.dib.2023.109896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 11/02/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024] Open
Abstract
The prevalence of mental fatigue is a noteworthy phenomenon that can affect individuals across diverse professions and working routines. This paper provides a comprehensive dataset of physiological signals obtained from 23 participants during their professional work and questionnaires to analyze mental fatigue. The questionnaires included demographic information and Chalder Fatigue Scale scores indicating mental and physical fatigue. Both physiological signal measurements and the Chalder Fatigue Scale were performed in two sessions, morning and evening. The present dataset encompasses diverse physiological signals, including electroencephalogram (EEG), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and 3-axis accelerometer (ACC) data. The NeuroSky MindWave EEG device was used for brain signals, and the Empatica E4 smart wristband was used for other signals. Measurements were carried out on individuals from four different occupational groups, such as academicians, technicians, computer engineers, and kitchen workers. The provision of comprehensive metadata supplements the dataset, thereby promoting inquiries about the neurophysiological concomitants of mental fatigue, autonomic activity patterns, and the repercussions of a cognitive burden on human proficiency in actual workplace settings. The accessibility of the aforementioned dataset serves to facilitate progress in the field of mental fatigue research while also laying the groundwork for the creation of customized fatigue evaluation techniques and interventions in diverse professional domains.
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Affiliation(s)
- Seyma Derdiyok
- Department of Computer Engineering, Yıldız Technical University, Istanbul, Turkey
| | - Fatma Patlar Akbulut
- Department of Software Engineering, Istanbul Kültür University, Istanbul, Turkey
| | - Cagatay Catal
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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Kalman JL, Burkhardt G, Samochowiec J, Gebhard C, Dom G, John M, Kilic O, Kurimay T, Lien L, Schouler-Ocak M, Vidal DP, Wiser J, Gaebel W, Volpe U, Falkai P. Digitalising mental health care: Practical recommendations from the European Psychiatric Association. Eur Psychiatry 2023; 67:e4. [PMID: 38086744 PMCID: PMC10790232 DOI: 10.1192/j.eurpsy.2023.2466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 09/17/2023] [Accepted: 10/12/2023] [Indexed: 01/06/2024] Open
Abstract
The digitalisation of mental health care is expected to improve the accessibility and quality of specialised treatment services and introduce innovative methods to study, assess, and monitor mental health disorders. In this narrative review and practical recommendation of the European Psychiatric Association (EPA), we aim to help healthcare providers and policymakers to navigate this rapidly evolving field. We provide an overview of the current scientific and implementation status across two major domains of digitalisation: i) digital mental health interventions and ii) digital phenotyping, discuss the potential of each domain to improve the accessibility and outcomes of mental health services, and highlight current challenges faced by researchers, clinicians, and service users. Furthermore, we make several recommendations meant to foster the widespread adoption of evidence-based digital solutions for mental health care in the member states of the EPA. To realise the vision of a digitalised, patient-centred, and data-driven mental health ecosystem, a number of implementation challenges must be considered and addressed, spanning from human, technical, ethical-legal, and economic barriers. The list of priority areas and action points our expert panel has identified could serve as a playbook for this process.
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Affiliation(s)
- Janos L. Kalman
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
| | - Gerrit Burkhardt
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
| | - Jerzy Samochowiec
- Department of Psychiatry, Pomeranian Medical University, Szczecin, Poland
| | | | - Geert Dom
- Collaborative Antwerp Psychiatric Research Institute (CAPRI), University of Antwerp, Antwerp, Belgium
| | - Miriam John
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
| | - Ozge Kilic
- Department of Psychiatry, Bezmialem Vakıf University Faculty of Medicine, Istanbul, Turkey
| | - Tamas Kurimay
- North-Buda Saint John Central Hospital, Buda Family Centred Mental Health Centre, Department of Psychiatry and Psychiatric Rehabilitation, Teaching Department of Semmelweis University, Budapest, Hungary
| | - Lars Lien
- National Advisory Unit on Concurrent Substance Abuse and Mental Health Disorders, Innlandet Hospital Trust, Hamar, Norway, Faculty of Social and Health Sciences, Inland Norway University of Applied Sciences, Elverum, Norway
| | - Meryam Schouler-Ocak
- Psychiatric University Clinic of Charité at St. Hedwig Hospital, Berlin, Germany
| | - Diego Palao Vidal
- Mental Health Service, Parc Taulí University Hospital, Unitat Mixta de Neurociència Traslacional I3PT-INc-UAB, Sabadell, Spain
- Department of Psychiatry and Forensic Medicine, Autonomous University of Barcelona, Cerdanyola del Vallès, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Jan Wiser
- CNWL NHS Foundation Trust, London, UK
| | - Wolfgang Gaebel
- WHO Collaborating Centre DEU-131, VR-Klinikum Düsseldorf, Department of Psychiatry and Psychotherapy, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Umberto Volpe
- Unit of Clinical Psychiatry, Department of Clinical Neurosciences/DIMSC, School of Medicine, Università Politecnica delle Marche, Ancona, Italy
| | - Peter Falkai
- Department of Psychiatry and Psychotherapy, LMU University Hospital, Munich, Germany
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Çöpürkaya Ç, Meriç E, Erik EB, Kocaçınar B, Akbulut FP, Catal C. Investigating the effects of stress on achievement: BIOSTRESS dataset. Data Brief 2023; 49:109297. [PMID: 37346930 PMCID: PMC10279542 DOI: 10.1016/j.dib.2023.109297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 05/27/2023] [Accepted: 05/31/2023] [Indexed: 06/23/2023] Open
Abstract
The effects of chronic stress on academic and professional achievement can have a substantial impact. This relationship is highlighted through a dataset that includes questionnaires and physiological data from a group of individuals. The questionnaire data of 48 individuals, the physiological data of 20 individuals during sessions with a psychologist, and the exam data of 8 individuals were analyzed. The questionnaire data collected includes demographic information and scores on the TOAD stress scale. Physiological data was captured using the Empatica e4, a wearable device, which measured various signals such as blood volume pulse, electrodermal activity, body temperature, interbeat intervals, heart rate, and 3-axis accelerometer data. These measurements were taken under different stress conditions, both high and low, during therapy sessions and an exam respectively. Overall, this study significantly contributes to our understanding of how stress affects achievement. By providing a large dataset consisting of questionnaires and physiological data, this research helps researchers gain a better understanding of the complex relationship between stress and achievement. It also enables them to develop innovative strategies for managing stress and enhancing academic and professional success.
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Affiliation(s)
- Çağla Çöpürkaya
- Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey
| | - Elif Meriç
- Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey
| | - Elif Berra Erik
- Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey
| | - Büşra Kocaçınar
- Department of Computer Engineering, Istanbul Kültür University, Istanbul, Turkey
| | - Fatma Patlar Akbulut
- Department of Software Engineering, Istanbul Kültür University, Istanbul, Turkey
| | - Cagatay Catal
- Department of Computer Science and Engineering, Qatar University, Doha, Qatar
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