Wearable Device Dataset from Induced Stress and Structured Exercise Sessions

This dataset comprises physiological signals recorded via a wearable device (Empatica E4) during structured acute stress induction and both aerobic and anaerobic exercise sessions. Collected metrics include blood volume pulse (BVP), accelerometer-based activity, skin temperature, and electrodermal a...

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Autores principales: Hongn, Andrea, Bosch, Facundo, Prada, Lara, Bonomini, Paula
Formato: Artículo de publicación periódica
Lenguaje:Inglés
Publicado: PhysioNet 2025
Materias:
WEARABLE SIGNALS, ACUTE STRESS, AEROBIC EXERCISE, ANAEROBIC EXERCISE, PHYSIOLOGICAL MONITORING, EMPATICA E4, SIGNAL PROCESSING, DATASET, NON-INVASIVE SENSING, MACHINE LEARNING
Acceso en línea:https://hdl.handle.net/20.500.14769/5109
https://doi.org/10.13026/he0v-tf17
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-20.500.14769-5109
record_format dspace
spelling I32-R138-20.500.14769-51092025-10-08T20:39:08Z Wearable Device Dataset from Induced Stress and Structured Exercise Sessions Hongn, Andrea Bosch, Facundo Prada, Lara Bonomini, Paula WEARABLE SIGNALS, ACUTE STRESS, AEROBIC EXERCISE, ANAEROBIC EXERCISE, PHYSIOLOGICAL MONITORING, EMPATICA E4, SIGNAL PROCESSING, DATASET, NON-INVASIVE SENSING, MACHINE LEARNING This dataset comprises physiological signals recorded via a wearable device (Empatica E4) during structured acute stress induction and both aerobic and anaerobic exercise sessions. Collected metrics include blood volume pulse (BVP), accelerometer-based activity, skin temperature, and electrodermal activity, along with self-reported stress levels. The stress protocol combines math and emotional tasks with rest intervals, while the exercise sessions involve defined cycling routines for aerobic and anaerobic conditions. The dataset includes recordings from 36 healthy volunteers during stress sessions, 30 participants for aerobic exercise, and 31 for anaerobic protocols. The data are organized into stress, aerobic, and anaerobic categories, with raw signal files (e.g., TEMP, EDA, BVP, ACC, IBI, HR) and tags for segmentation. Demographic information (age, weight, height) and self-reported stress scores are also included. Some limitations such as incomplete sessions or signal artifacts are documented. This resource is intended for research in stress and exercise detection, classification, and physiological signal processing, facilitating the development of machine learning models to distinguish among stress, aerobic activity, and anaerobic activity from noninvasive wearable sensor data. 2025-10-08T20:39:06Z 2025-10-08T20:39:06Z 2025-06-24 Artículo de publicación periódica https://hdl.handle.net/20.500.14769/5109 https://doi.org/10.13026/he0v-tf17 en Scientific Data; 12(1). PhysioNet
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic WEARABLE SIGNALS, ACUTE STRESS, AEROBIC EXERCISE, ANAEROBIC EXERCISE, PHYSIOLOGICAL MONITORING, EMPATICA E4, SIGNAL PROCESSING, DATASET, NON-INVASIVE SENSING, MACHINE LEARNING
spellingShingle WEARABLE SIGNALS, ACUTE STRESS, AEROBIC EXERCISE, ANAEROBIC EXERCISE, PHYSIOLOGICAL MONITORING, EMPATICA E4, SIGNAL PROCESSING, DATASET, NON-INVASIVE SENSING, MACHINE LEARNING
Hongn, Andrea
Bosch, Facundo
Prada, Lara
Bonomini, Paula
Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
topic_facet WEARABLE SIGNALS, ACUTE STRESS, AEROBIC EXERCISE, ANAEROBIC EXERCISE, PHYSIOLOGICAL MONITORING, EMPATICA E4, SIGNAL PROCESSING, DATASET, NON-INVASIVE SENSING, MACHINE LEARNING
description This dataset comprises physiological signals recorded via a wearable device (Empatica E4) during structured acute stress induction and both aerobic and anaerobic exercise sessions. Collected metrics include blood volume pulse (BVP), accelerometer-based activity, skin temperature, and electrodermal activity, along with self-reported stress levels. The stress protocol combines math and emotional tasks with rest intervals, while the exercise sessions involve defined cycling routines for aerobic and anaerobic conditions. The dataset includes recordings from 36 healthy volunteers during stress sessions, 30 participants for aerobic exercise, and 31 for anaerobic protocols. The data are organized into stress, aerobic, and anaerobic categories, with raw signal files (e.g., TEMP, EDA, BVP, ACC, IBI, HR) and tags for segmentation. Demographic information (age, weight, height) and self-reported stress scores are also included. Some limitations such as incomplete sessions or signal artifacts are documented. This resource is intended for research in stress and exercise detection, classification, and physiological signal processing, facilitating the development of machine learning models to distinguish among stress, aerobic activity, and anaerobic activity from noninvasive wearable sensor data.
format Artículo de publicación periódica
author Hongn, Andrea
Bosch, Facundo
Prada, Lara
Bonomini, Paula
author_facet Hongn, Andrea
Bosch, Facundo
Prada, Lara
Bonomini, Paula
author_sort Hongn, Andrea
title Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
title_short Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
title_full Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
title_fullStr Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
title_full_unstemmed Wearable Device Dataset from Induced Stress and Structured Exercise Sessions
title_sort wearable device dataset from induced stress and structured exercise sessions
publisher PhysioNet
publishDate 2025
url https://hdl.handle.net/20.500.14769/5109
https://doi.org/10.13026/he0v-tf17
work_keys_str_mv AT hongnandrea wearabledevicedatasetfrominducedstressandstructuredexercisesessions
AT boschfacundo wearabledevicedatasetfrominducedstressandstructuredexercisesessions
AT pradalara wearabledevicedatasetfrominducedstressandstructuredexercisesessions
AT bonominipaula wearabledevicedatasetfrominducedstressandstructuredexercisesessions
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