Advances in Internet of Things (IoT) devices and in Machine Learning (ML) applications can provide valuable insights and predictions on personal health by optimizing data generation and processing. Nevertheless, the flow of data about the health status of a patient brings a variety of technical, legal and economic challenges that need to be addressed through an interdisciplinary approach. In this context, based on the action research methodology, the paper introduces an exemplary health-related activity recognition platform based on IoT, developed as a part of European-funded project Horizon 2020 in collaboration with academia and industry. The platform proposes innovative solutions on how personal healthcare data can be processed and analysed, protecting users’ privacy. The main strength of the platform is the interdisciplinary approach used within a triple-helix model, involving a variety of institutions, companies and researchers from different academic fields. In this perspective, the paper shows the potential that the integration of IoT and ML models have to offer and the main challenges that still need to be addressed.

Interdisciplinary research unlocking innovative solutions in healthcare

Dominique Lepore;Yuan Li;Nuoya Chen;Francesca Spigarelli.
2023-01-01

Abstract

Advances in Internet of Things (IoT) devices and in Machine Learning (ML) applications can provide valuable insights and predictions on personal health by optimizing data generation and processing. Nevertheless, the flow of data about the health status of a patient brings a variety of technical, legal and economic challenges that need to be addressed through an interdisciplinary approach. In this context, based on the action research methodology, the paper introduces an exemplary health-related activity recognition platform based on IoT, developed as a part of European-funded project Horizon 2020 in collaboration with academia and industry. The platform proposes innovative solutions on how personal healthcare data can be processed and analysed, protecting users’ privacy. The main strength of the platform is the interdisciplinary approach used within a triple-helix model, involving a variety of institutions, companies and researchers from different academic fields. In this perspective, the paper shows the potential that the integration of IoT and ML models have to offer and the main challenges that still need to be addressed.
2023
Elsevier
Internazionale
https://www.sciencedirect.com/science/article/pii/S016649722200058X?via=ihub
File in questo prodotto:
File Dimensione Formato  
Technovation_2023.pdf

accesso aperto

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Creative commons
Dimensione 2.33 MB
Formato Adobe PDF
2.33 MB Adobe PDF Visualizza/Apri

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/11393/294725
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 19
  • ???jsp.display-item.citation.isi??? 9
social impact