Recent advances in wearable microelectronics and new neural networks paradigms, capable to evolve and learn online such as the Evolving Fuzzy Neural Network (EFuNN), enable the deploy of biofeedback-based applications. The missed physiologic response could be recovered by measuring uninvasively the vital signs such as the heart rate, the bio impedance, the body temperature, the motion activity, the blood pressure, the blood oxygenation and the respiration rate. Then, the prediction could be performed applying the evolving ANN paradigms. The simulation of a wearable biofeedback system has been executed applying the Evolving Fuzzy Neural Network (EFuNN) paradigm for prediction. An highly integrated wearable microelectronic device for uninvasively vital signs measurement has been deployed. Simulation results demonstrate that biofeedback control model could be an effective reference design that enables short and long-term e-health prediction. The biofeedback framework was been then defined.

Biofeedback: e-health prediction based on evolving fuzzy neural network and wearable technologies / M. Malcangi, G. Nano. - In: EVOLVING SYSTEMS. - ISSN 1868-6478. - 12:3(2021 Sep), pp. 645-653. [10.1007/s12530-021-09374-5]

Biofeedback: e-health prediction based on evolving fuzzy neural network and wearable technologies

M. Malcangi
;
G. Nano
2021

Abstract

Recent advances in wearable microelectronics and new neural networks paradigms, capable to evolve and learn online such as the Evolving Fuzzy Neural Network (EFuNN), enable the deploy of biofeedback-based applications. The missed physiologic response could be recovered by measuring uninvasively the vital signs such as the heart rate, the bio impedance, the body temperature, the motion activity, the blood pressure, the blood oxygenation and the respiration rate. Then, the prediction could be performed applying the evolving ANN paradigms. The simulation of a wearable biofeedback system has been executed applying the Evolving Fuzzy Neural Network (EFuNN) paradigm for prediction. An highly integrated wearable microelectronic device for uninvasively vital signs measurement has been deployed. Simulation results demonstrate that biofeedback control model could be an effective reference design that enables short and long-term e-health prediction. The biofeedback framework was been then defined.
Biofeedback; EFuNN; Ehealth; Evolving learning; Online learning; Prediction; Vital signs; Wearables
Settore INF/01 - Informatica
20-mar-2021
Article (author)
File in questo prodotto:
File Dimensione Formato  
Malcangi-Nano2021_Article_BiofeedbackE-healthPredictionB.pdf

accesso aperto

Tipologia: Publisher's version/PDF
Dimensione 992.37 kB
Formato Adobe PDF
992.37 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/878687
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 3
social impact