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In this paper an accessible indoor navigation data model suitable for multi-model NoSQL databases is proposed, based on iLOC and iACC ontologies. We consider our previous observations about practicality and performance of labeled property graph data models used for accessible indoor wayfinding and suggest multiple ways of improvement by using a multi-model approach. A very important aim of the new data model is to provide a structure to be able to run fully customizable wayfinding queries with respect to accessibility needs of disabled users.

In the paper the optimal representation of data based on iLOC and iACC ontologies in labeled property graph data model is explored. We describe and analyse the process where RDF-type data is transformed into Neo4j. The focus is on a specific subtask of the transformation, namely how instance-of a subclass type relationships can be expressed in labeled property graphs. Different solutions of the problem are described and analysed from the perspective of path traversal effectiveness.

In this paper the creation of a navigation application solely based on data is described. The application provides indoor navigation features at the Honved Hospital in Budapest. In the first phase of the project the indoor location data is given in the form of Linked Open Data based on iLOC and hLOC ontologies. The RDF type data is migrated into Neo4j database, on which the application is based. The article describes the process, the technologies and main milestones of the application development.

Nowadays, there are numerous types of diets that aim to improve the quality of life, health and longevity of people. However, these diets typically involve a strictly planned regime, which can be hard to get used to or even to follow through at all, due to the sudden nature of the change. In this paper, the framework for an Intelligent Space application is proposed that helps its users to achieve a healthier diet in the long term by introducing small, gradual changes into their consumption habits. The application observes the daily nutrition intake of its users, applies data mining in order to learn their personal tastes, and educates them about the effects of their current diet on their health. Then it analyzes the knowledge base to find different food or drink items that align with the perceived preferences, while also add to the balance of the daily nutrition of the users considering their physical properties, activities, and health conditions (e.g. diabetes, celiac disease, food allergies, etc). Finally, the system uses the findings to make suggestions about adding items from the consumption list, or change one item to another.

In this paper an accessibility extension is described for an ontology supporting indoor navigation called iLOC. In iLOC, a basic support for accessible route planning can be found, but the accessibility description of objects like doors, floors, restrooms etc. were based on measurements and accessibility criteria included in the Hungarian government regulations. This regulation is designed to ensure a safe and unobstructed environment for every motion disabled person with every kind of wheelchair, but it can be unnecessarily restrictive for some users. Our accessibility extension adds classes and properties that can be connected to the classes of the original iLOC, and allows of a detailed description of the objects in the building in terms of attributes such as width, height or quantity.

The paper provides a data model in the form of ontology which includes the indoor location description of hospitals, the indoor navigation features and the accessibility attributes for people with motion disabilities. The possible use of the ontology is demonstrated by outlining some SPARQL queries for the navigation features of future applications.

Chronic neuromuscular diseases often cause dysarthria (speech distortions, impaired articulation, etc.), that becomes more severe over time. This aspect of the disease represents a serious problem in voice-controlled smart home systems. Medical research suggests that some speech features are impaired considerably, while others remain relatively unharmed. Therefore, it is possible to create a distance metric based on medical data that measures difference between two speech commands in a dysarthria-specific way: the contribution of various features to the distance is based on the extent of dysarthric impairment. Specifying a minimal distance between speech commands contributes to a more effective recognition during later stages of the disease.

Dysarthria is a speech disorder that mostly occurs as a symptom of neurodegenerative and other neuromuscular diseases. The speech of patients with dysarthria becomes distorted, the articulation of phonemes (especially that of consonants) is poor, the intelligibility and naturalness are impaired. Because dysarthria is progressive (similarly to the other symptoms of the main disease), patients may have difficulties using speech-controlled Ambient Assisted Living systems that could be a great help for them in daily life. In this paper, an adaptive speech recognition framework is introduced that is able to handle gradually occurring changes in the speech quality of the user. The presented technique can adapt to these changes while the speech interpretation accuracy of the system will not decrease, even in cases of noisy or incorrect training data.