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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.

A variety of technology enhanced teaching strategies and learning activities have been applied in education, including assessment mechanisms. In this paper, we aim to examine the extent to which peer assessment promotes deep learning and favours the incremental learning of the concepts presented throughout a traditional introductory programming course. In particular, we want to examine how the enactment of the peer-assessor and peer-assessee roles is associated with students’ learning improvements, after enacting recip-rocal peer assessment. We illustrate a case study based on a novel web-based peer assessment tool to improve engagement and learning outcomes in a pro-gramming course. We want to assess whether programming skills are growing through peer review and whether exposure to different programming tech-niques is helpful to students. One of the most remarkable findings of our ex-perience was that students reported that assessing others’ work was an ex-tremely valuable learning activity.

The huge amounts of self-tracked health data collected by Internet of Things (IoT) fitness devices offer important opportunities to the research community. If properly exploited, IoT health and fitness datasets can help to gain valuable insights into the human health in order to provide better healthcare.However, IoT health data come from a variety of different heterogeneous sources and in proprietary formats, which means that they require an integration process, normally manually done by domain experts, in order to be analysed. This task is not only significantly time consuming but in many cases, error prone.In this study, we designed and developed a web platform for collecting and publishing IoT health and fitness datasets according to Linked Data principles. We leveraged the IFO ontology and the Semantic Web technologies to make the IoT health and fitness datasets freely available to the community in a shared, semantically meaningful, easily discoverable, and reusable manner.The system introduced in this article shows that Semantic Web technologies can be a viable and comprehensive solution for describing, integrating and sharing heterogeneous IoT datasets, thus overcoming the issues of data silos that nowadays dominate the IoT landscape.

Ontologies play an important role in knowledge-intensive contexts such as nutrition, personalised dietary, diet-sensitive disease conditions management and sport activities. Ontologies offer a shared data model for concept integration making possible to actuate automatic data analysis processes such as inference reasoning and allow interoperable communication among heterogeneous systems. In this paper, we propose Food Data Manager, a dietary consultation system which aims to improve the life quality of both healthy people and individuals affected by chronic diet-related diseases. Food Data Manager is an ontology-based system whereby we can automatically reason with food and its properties through inference engines in order to better assist users in making the correct choices for their particular health status, age, lifestyle, preferences, etc. In particular, we developed a set of semantic rules to transfer human dietary and nutrition expertise into machine understandable knowledge.

In knowledge intensive nutrition-related contexts, such as personalised dietary, diet-sensitive diseases management and sport supplementation, ontologies play an important role. In this paper, we propose an ontology-based consultation system which aims to improve the life quality of both healthy people and individuals affected by chronic diet-related diseases. We developed a system which is capable of transferring human dietary and nutrition expertise into machine understandable knowledge through a set of semantic rules in order to better assist users in making the correct nutritional choices for their particular health status, age, lifestyle and food preferences. Our system makes use of open data, published ontologies, domain knowledge and IoT data to construct a domain representation consisting of unified concepts and instances suitable for reasoning processes. We described how several knowledge bases in knowledge-intensive contexts can be integrated to provide a unified structured and precise representation of heterogeneous information to provide better diet recommendation to individuals.

Nowadays, the enormous volume of health and fitness data gathered from IoT wearable devices offers favourable opportunities to the research community. For instance, it can be exploited using sophisticated data analysis techniques, such as automatic reasoning, to find patterns and, extract information and new knowledge in order to enhance decision-making and deliver better healthcare. However, due to the high heterogeneity of data representation formats, the IoT healthcare landscape is characterised by an ubiquitous presence of data silos which prevents users and clinicians from obtaining a consistent representation of the whole knowledge. Semantic web technologies, such as ontologies and inference rules, have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (1) consistently represent health and fitness data from heterogeneous IoT sources; (2) integrate and exchange them; and (3) enable automatic reasoning by inference engines.