Towards exergaming commons: composing the exergame ontology for publishing open game data

Abstract

BackgroundIt has been shown that exergames have multiple benefits for physical, mental and cognitive health. Only recently, however, researchers have started considering them as health monitoring tools, through collection and analysis of game metrics data. In light of this and initiatives like the Quantified Self, there is an emerging need to open the data produced by health games and their associated metrics in order for them to be evaluated by the research community in an attempt to quantify their potential health, cognitive and physiological benefits.MethodsWe have developed an ontology that describes exergames using the Web Ontology Language (OWL); it is available at http://purl.org/net/exergame/ns#. After an investigation of key components of exergames, relevant ontologies were incorporated, while necessary classes and properties were defined to model these components. A JavaScript framework was also developed in order to apply the ontology to online exergames. Finally, a SPARQL Endpoint is provided to enable open data access to potential clients through the web.ResultsExergame components include details for players, game sessions, as well as, data produced during these game-playing sessions. The description of the game includes elements such as goals, game controllers and presentation hardware used; what is more, concepts from already existing ontologies are reused/repurposed. Game sessions include information related to the player, the date and venue where the game was played, as well as, the results/scores that were produced/achieved. These games are subsequently played by 14 users in multiple game sessions and the results derived from these sessions are published in a triplestore as open data.ConclusionsWe model concepts related to exergames by providing a standardized structure for reference and comparison. This is the first work that publishes data from actual exergame sessions on the web, facilitating the integration and analysis of the data, while allowing open data access through the web in an effort to enable the concept of Open Trials for Active and Healthy Ageing.

Linked Open Data in Health and Clinical Care A Review of the Literature

“…As reported in Table 1, and in Figure 1, the literature search identified 37 citations in PubMed and 2806 citations in Scopus applying ¡"Linked Open Data"[All Fields]¿ as a search string. Specifying the search strings according to the aim of the review, and the inclusion criteria ( Figure 1), 9 papers have been considered and examined [23][24][25][26][27][28][29][30][31].…”

“…In 4 out of 9 papers, a framework -or system -supporting LOD datasets is developed locally for specific purposes (papers 2, 3, 5, and 7) [24,25,27,29], see Table 2. In papers, 6, 8, and 9 the aim is the development of ontologies in RDF format [28,30,31]. Finally, in the paper 1 [23], a database has been developed with open data access, without Semantic Web technologies.…”

“…As for the data sources involved, papers 2, 3, 5, and 7 [24,25,27,29], include data collected locally and LOD sources to use for integrating or enriching those data in RDF format. In papers, 6, 8, 9, some LOD ontologies have been used as a base for the developed ones [28,30,31]. In paper 1, LOD sources have not been used (Table 2).…”

“…In paper 1, LOD sources have not been used (Table 2). Finally, the publication year for 3 out of 9 papers (papers 1, 2, and 3) [23][24][25] is 2013, for 3 out of 9 papers (papers 4, 5, and 6) [26-28] is 2014, for 1 out of 9 papers (paper 7) [29] is 2015, and for 2 out of 9 papers (paper 8, and 9) [30,31] is 2016, Table 2.…”

Improving Citizen Science Games through Open Analytics Data

“…(Devlin et al, 2014) propose that the analysis of metrics from any game could potentially be of scientific interest, "potentially making all games into scientific discovery games." (Bamparopoulos, Konstantinidis, Bratsas, & Bamidis, 2016) argue that it is imperative to open data gathered by exercise games so that researchers may evaluate the effectiveness of such data as a healthmonitoring tool. Releasing the algorithms that calculate caloric expenditure and heart rate would also help to establish the validity of the data (Staiano & Calvert, 2011).…”