Interdisciplinary research unlocking innovative solutions in healthcare
Dominique Lepore, Koustabh Dolui, Oleksandr Tomashchuk, Heereen Shim, Chetanya Puri, Yuan Li, Nuoya Chen, Francesca Spigarelli
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.