Gaetano Valenza
University of Pisa
326 Papers
683 Citations
Gaetano Valenza is an academic researcher from University of Pisa. The author has contributed to research in topics: Heartbeat & Computer science. The author has an hindex of 35, co-authored 262 publications. Previous affiliations of Gaetano Valenza include Massachusetts Institute of Technology & University of Essex.
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Papers
cvxEDA: A Convex Optimization Approach to Electrodermal Activity Processing
TL;DR: A novel algorithm for the analysis of electrodermal activity (EDA) using methods of convex optimization is reported on, showing good performance of the proposed method and suggesting promising future applicability, e.g., in the field of affective computing.
Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors.
Javier Marín-Morales,Juan Luis Higuera-Trujillo,Alberto Greco,Jaime Guixeres,Carmen Llinares,Enzo Pasquale Scilingo,Mariano Alcañiz,Gaetano Valenza +7 more
TL;DR: The findings validate the use of Immersive Virtual Environments to elicit and automatically recognize different emotional states from neural and cardiac dynamics; this development could have novel applications in fields as diverse as Architecture, Health, Education and Videogames.
The Role of Nonlinear Dynamics in Affective Valence and Arousal Recognition
TL;DR: An automatic multiclass arousal/valence classifier is implemented comparing performance when extracted features from nonlinear methods are used as an alternative to standard features and results show that, when nonlinearly extracted features are used, the percentages of successful recognition dramatically increase.
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Recognizing Emotions Induced by Affective Sounds through Heart Rate Variability
TL;DR: This paper reports on how emotional states elicited by affective sounds can be effectively recognized by means of estimates of Autonomic Nervous System (ANS) dynamics.
Wearable Monitoring for Mood Recognition in Bipolar Disorder Based on History-Dependent Long-Term Heart Rate Variability Analysis
Gaetano Valenza,Mimma Nardelli,Antonio Lanata,Claudio Gentili,Gilles Bertschy,Rita Paradiso,Enzo Pasquale Scilingo +6 more
TL;DR: Experimental results demonstrate that the novel concept of personalized and pervasive monitoring constitutes a viable and robust clinical decision support system for bipolar disorders recognizing mood states with a total classification accuracy up to 95.81%.