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Abstract
The huge volume of data gathered from wearable fitness devices and wellness appliances, if effectively analysed and integrated, can be exploited to improve clinical decision making and to stimulate promising applications, as they can provide good measures of everyday patient behaviour and lifestyle. However, several obstacles currently limit the true exploitation of these opportunities. In particular, the healthcare landscape is characterised by a pervasive presence of data silos which prevent users and healthcare professionals from obtaining an overall view of the knowledge, mainly due to the lack of device interoperability and data representation format heterogeneity. This work focuses on current, important needs in self-tracked health data modelling, and summarises challenges and opportunities that will characterise the community in the upcoming years. The paper describes a virtually integrated approach using standard Web Semantic technologies and Linked Open Data to cope with heterogeneous health data integration. The proposed approach is verified using data collected from several IoT fitness vendors to form a standard context-aware resource graph, and linking other health ontologies and open projects. We developed a web portal for integrating, sharing and analysing through a customisable dashboard heterogeneous IoT health and fitness data. In this way, we are able to map information onto an integrated domain model by providing support for logical reasoning.
Highlights
Nowadays, wearable devices have significantly grown in popularity and recent statistics have shown that around 50% of people in developed countries make use of these devices to monitor fitness or physical activity [1]
We propose to convert heterogeneous Internet of Things (IoT) raw data collected by a multitude of different devices into Resource Description Framework (RDF) graphs [7]
To further improve the usability of semantically integrated data, starting from the early work described in [11], we developed a Linked Open Data (LOD)-based web portal in order to collect health and fitness data gathered from consumer health IoT devices, and make them freely available on the Web
Summary
Wearable devices have significantly grown in popularity and recent statistics have shown that around 50% of people in developed countries make use of these devices to monitor fitness or physical activity [1]. The worldwide recorded data come from a variety of different heterogeneous sources and are represented with their own proprietary format depending on the device’s manufacturer (Fig. 1a) This heterogeneity characterises all the Internet of Things (IoT) health and fitness datasets and, together with the typical huge volume of data, makes data sharing and integration extremely difficult. The LOD portal may become a reference point for collecting, sharing and analysing IoT health and fitness data in structured format, accessible to domain experts, scientists and the web community without any restrictions by any form of patent or licensing. From other platforms for sharing personal health data (PHD), such as Kaggle or Open Humans which redistribute users’ data directly in raw formats (i.e., unstructured or semi-structured serialisation formats), a novel aspect of our portal consists in providing a semantic representation of the IoT datasets. The obtained standard context-aware resource graph is linked to other health ontologies and open projects to map information onto a specialized domain model by providing support for logical reasoning
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