Linked Open Health-related Fitness Data System

Tracking #: 2092-3305

This paper is currently under review
Authors: 
Roberto Reda
Filippo Piccinini
Antonella Carbonaro

Responsible editor: 
Guest Editors Sensors Observations 2018

Submission type: 
Full Paper
Abstract: 
In recent years, the market for the Internet of Things (IoT) has seen a proliferation of health wearable devices such as smart watches, fitness bands, and wellness appliances continuously collecting and storing a huge amount of physiological parameters. These data can be potentially exploited by the research community in order to gain valuable insights into our health systems. However, IoT self-tracked health data come from a variety of different heterogeneous sources and in proprietary formats, which often lead them to remain confined into separate data silos. Thus, when it comes to analysing IoT health and fitness datasets, data collection and data integration have to be done manually by domain experts. This time consuming and prone to error process significantly hampers an efficient exploitation of the information available. Semantic Web technologies can be a viable and comprehensive solution for describing, integrating and sharing heterogeneous IoT datasets. The aim of this work is to propose a web platform for the standardisation of data collection and integration to permit users to get a common view of the available information. To achieve our purpose we designed the IoT Fitness Ontology and we leveraged Semantic Web technologies in order to make IoT health and fitness datasets freely available to the community in a shared, semantically meaningful, and reusable manner.
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Under Review