Ultra-Short-Term HRV and EDA Features for Machine-Learning-Based Arousal Detection in Exergaming
Gianluca AmprimoPolytechnic University of TurinGiulia MasiPolytechnic University of TurinIrene RechichiPolytechnic University of TurinLuca VismaraUniversity of TurinSofia TaginiIRCCS Istituto Auxologico Italiano, Ospedale San GiuseppeBianchi, LauraIRCCS Istituto Auxologico Italiano, Ospedale San GiuseppeFederica ScarpinaUniversity of TurinLorenzo PrianoUniversity of TurinGabriella OlmoPolytechnic University of TurinClaudia Ferraris
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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.
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