Wearable Physiological Signals under Acute Stress and Exercise Conditions
Abstract In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures elec...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-04845-9 |
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| author | Andrea Hongn Facundo Bosch Lara Eleonora Prado José Manuel Ferrández María Paula Bonomini |
| author_facet | Andrea Hongn Facundo Bosch Lara Eleonora Prado José Manuel Ferrández María Paula Bonomini |
| author_sort | Andrea Hongn |
| collection | DOAJ |
| description | Abstract In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures electrodermal activity, skin temperature, three-axis accelerometry and blood volume pulse, from which heart rate and heart rate variability features can be derived. A stress induction protocol was designed using mathematical and emotional tasks to elicit physiological responses. For aerobic and anaerobic exercise, a stationary bike routine was developed to distinguish between the two types of activity. The dataset includes records from 36 healthy individuals during the stress protocol, 30 during aerobic exercise, and 31 during anaerobic exercise. Several machine learning algorithms were applied to validate the dataset, with XGBoost achieving an accuracy of 93% in classifying stress versus rest, 91% in distinguishing between aerobic and anaerobic exercise, and 84% in a four-label classification task involving stress, rest, aerobic, and anaerobic activities. The dataset is publicly available for further research. |
| format | Article |
| id | doaj-art-d258ca244b7b4b4d9269354ce5db16dc |
| institution | DOAJ |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-d258ca244b7b4b4d9269354ce5db16dc2025-08-20T03:22:00ZengNature PortfolioScientific Data2052-44632025-03-0112111010.1038/s41597-025-04845-9Wearable Physiological Signals under Acute Stress and Exercise ConditionsAndrea Hongn0Facundo Bosch1Lara Eleonora Prado2José Manuel Ferrández3María Paula Bonomini4Universidad de Buenos Aires. Facultad de Ingeniería, Instituto de Ingeniería Biomédica (IIBM)Departamento de Ciencias de la Vida, Instituto Tecnológico de Buenos Aires (ITBA)Departamento de Ciencias de la Vida, Instituto Tecnológico de Buenos Aires (ITBA)Departamento de Electrónica, Tecnología de Computadoras y Proyectos, Universidad Politécnica de CartagenaInstituto Argentino de Matemática “Alberto P. Calderón” (IAM), CONICETAbstract In this work, a novel dataset containing physiological signals recorded non invasevely during structured acute stress induction, as well as aerobic and anaerobic exercise sessions is presented. The physiological data were collected using the Empatica E4, a wearable device that measures electrodermal activity, skin temperature, three-axis accelerometry and blood volume pulse, from which heart rate and heart rate variability features can be derived. A stress induction protocol was designed using mathematical and emotional tasks to elicit physiological responses. For aerobic and anaerobic exercise, a stationary bike routine was developed to distinguish between the two types of activity. The dataset includes records from 36 healthy individuals during the stress protocol, 30 during aerobic exercise, and 31 during anaerobic exercise. Several machine learning algorithms were applied to validate the dataset, with XGBoost achieving an accuracy of 93% in classifying stress versus rest, 91% in distinguishing between aerobic and anaerobic exercise, and 84% in a four-label classification task involving stress, rest, aerobic, and anaerobic activities. The dataset is publicly available for further research.https://doi.org/10.1038/s41597-025-04845-9 |
| spellingShingle | Andrea Hongn Facundo Bosch Lara Eleonora Prado José Manuel Ferrández María Paula Bonomini Wearable Physiological Signals under Acute Stress and Exercise Conditions Scientific Data |
| title | Wearable Physiological Signals under Acute Stress and Exercise Conditions |
| title_full | Wearable Physiological Signals under Acute Stress and Exercise Conditions |
| title_fullStr | Wearable Physiological Signals under Acute Stress and Exercise Conditions |
| title_full_unstemmed | Wearable Physiological Signals under Acute Stress and Exercise Conditions |
| title_short | Wearable Physiological Signals under Acute Stress and Exercise Conditions |
| title_sort | wearable physiological signals under acute stress and exercise conditions |
| url | https://doi.org/10.1038/s41597-025-04845-9 |
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