Wearable Device Dataset from Induced Stress and Structured Exercise Sessions

Registro completo

Título

Wearable Device Dataset from Induced Stress and Structured Exercise Sessions

Autor(es)

Hongn, Andrea; Bosch, Facundo; Prado, Lara Eleonora; Bonomini, Maria Paula

Afiliación(es) del/de los autor(es)

Hongn, Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina
Bosch, Facundo. Instituto Tecnológico de Buenos Aires; Argentina
Prado, Lara Eleonora. Instituto Tecnológico de Buenos Aires; Argentina
Bonomini, Maria Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentina. Upct Universidad Politécnica de Cartagena;

Resumen

This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification.

Año de publicación

2024

Idioma

inglés

Formato (Tipo MIME)

application/octet-stream
application/vnd.rar
text/plain

Clasificación temática de acuerdo a la FORD

Ingeniería médica

Condiciones de uso

Disponible en acceso abierto bajo licencia Creative Commons https://creativecommons.org/licenses/by-nc-sa/2.5/ar/

Repositorio digital

CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas