Ultra-Short-Term HRV and EDA Features for Machine-Learning-Based Arousal Detection in Exergaming

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: Gianluca Amprimo, Giulia Masi, Irene Rechichi, Luca Vismara, Sofia Tagini, Bianchi, Laura, Federica Scarpina, Lorenzo Priano, Gabriella Olmo, Claudia Ferraris
DOI: 10.5281/zenodo.18515714
Cited by: 0

Abstract

HRV and EDA features collected for the experiments reported in the manuscript: "Influence of High Allostatic Load and Obesity on Ultra-Short-Term HRV and EDA Features for Machine-Learning-Based Arousal Detection in Exergaming". The dataset includes computed features per protocol segment and group information (PwO or HP). Further clinical information (e.g., sex, age, BMI) about patients are not provided due to privacy constraints, but can be requested from the data curators. Latest version includes detrended EDA features, to remove potential accumulation effects.

Fields of Study & Subjects

Emotion and Mood Recognition Heart Rate Variability and Autonomic Control Non-Invasive Vital Sign Monitoring

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
7 February 2026
Language
EN
Document type
Dataset