PPG and EDA dataset collected with Empatica E4 for stress assessment
In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Therefore, we collected physiological signals (blood pressure volume an...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924000751 |
| _version_ | 1827313754094698496 |
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| author | Sara Campanella Ayham Altaleb Alberto Belli Paola Pierleoni Lorenzo Palma |
| author_facet | Sara Campanella Ayham Altaleb Alberto Belli Paola Pierleoni Lorenzo Palma |
| author_sort | Sara Campanella |
| collection | DOAJ |
| description | In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Therefore, we collected physiological signals (blood pressure volume and electrodermal activities), using Empatica E4, from 29 subjects. A personalized protocol was developed to cause cognitive, mental, and psychological stressors since they are the ones that can be experienced in working or academic environment. We also propose a pipeline to clean and process these two signals to maximize the quality of further analysis. This study aids in the comprehension of the complex connection between stress and working situations by offering a sizable dataset made up of different physiological data. It additionally enables them to create cutting-edge stress-reduction techniques and improving professional achievement while lessening the negative impact of stress on welfare. |
| first_indexed | 2024-03-08T08:26:47Z |
| format | Article |
| id | doaj.art-c5fbf6588cfb4ed29fcdee849491c895 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-04-24T22:20:40Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-c5fbf6588cfb4ed29fcdee849491c8952024-03-20T06:09:43ZengElsevierData in Brief2352-34092024-04-0153110102PPG and EDA dataset collected with Empatica E4 for stress assessmentSara Campanella0Ayham Altaleb1Alberto Belli2Paola Pierleoni3Lorenzo Palma4Department of Information Engineering (DII), Università Politecnica delle Marche, 60131, Ancona, ItalyDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131, Ancona, ItalyDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131, Ancona, ItalyDepartment of Information Engineering (DII), Università Politecnica delle Marche, 60131, Ancona, ItalyCorresponding author.; Department of Information Engineering (DII), Università Politecnica delle Marche, 60131, Ancona, ItalyIn response to challenging circumstances, the human body can experience marked levels of anxiety and distress. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Therefore, we collected physiological signals (blood pressure volume and electrodermal activities), using Empatica E4, from 29 subjects. A personalized protocol was developed to cause cognitive, mental, and psychological stressors since they are the ones that can be experienced in working or academic environment. We also propose a pipeline to clean and process these two signals to maximize the quality of further analysis. This study aids in the comprehension of the complex connection between stress and working situations by offering a sizable dataset made up of different physiological data. It additionally enables them to create cutting-edge stress-reduction techniques and improving professional achievement while lessening the negative impact of stress on welfare.http://www.sciencedirect.com/science/article/pii/S2352340924000751Wearable sensorsElectronic devicesElectrodermal activityPhotoplethysmographyStress detection |
| spellingShingle | Sara Campanella Ayham Altaleb Alberto Belli Paola Pierleoni Lorenzo Palma PPG and EDA dataset collected with Empatica E4 for stress assessment Data in Brief Wearable sensors Electronic devices Electrodermal activity Photoplethysmography Stress detection |
| title | PPG and EDA dataset collected with Empatica E4 for stress assessment |
| title_full | PPG and EDA dataset collected with Empatica E4 for stress assessment |
| title_fullStr | PPG and EDA dataset collected with Empatica E4 for stress assessment |
| title_full_unstemmed | PPG and EDA dataset collected with Empatica E4 for stress assessment |
| title_short | PPG and EDA dataset collected with Empatica E4 for stress assessment |
| title_sort | ppg and eda dataset collected with empatica e4 for stress assessment |
| topic | Wearable sensors Electronic devices Electrodermal activity Photoplethysmography Stress detection |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924000751 |
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