Prediction of state anxiety by machine learning applied to photoplethysmography data
David Perpetuini(University of Chieti-Pescara), Arcangelo Merla(University of Chieti-Pescara), Sabrina Conoci(University of Messina), Chiara Filippini(University of Chieti-Pescara), Vincenzo Vinciguerra(STMicroelectronics (Switzerland)), Vincenzo Perciavalle(University of Catania), Antonio Maria Chiarelli(University of Chieti-Pescara), Sabina Gallina(University of Chieti-Pescara), Simona Massimino(University of Catania), Daniela Cardone(University of Chieti-Pescara), Valentina Bucciarelli(Azienda Ospedaliero Universitaria Ospedali Riuniti), Sergio Rinella(University of Catania), P. G. Fallica(STMicroelectronics (Italy)), Francesco Bianco
Cited by 42
Related Papers
Quarantine during COVID-19 outbreak: Changes in diet and physical activity increase the risk of cardiovascular disease
|Nutrition Metabolism and Cardiovascular Diseases|2020|546
Stretchable wireless system for sweat pH monitoring
|Biosensors and Bioelectronics|2018|327
Role of the energy transfer in the optical properties of undoped and Er-doped interacting Si nanocrystals
|Journal of Applied Physics|2001|313
The excitation mechanism of rare-earth ions in silicon nanocrystals
|Applied Physics A|1999|242
Quantum confinement and recombination dynamics in silicon nanocrystals embedded in Si/SiO2 superlattices
|Journal of Applied Physics|2000|194