Stelios Andreadis

Utilizing a Wristband Sensor to Measure the Stress Level for People with Dementia

Stress is a common problem that affects most people with dementia and their caregivers. Stress symptoms for people with dementia are often measured by answering a checklist of questions by the clinical staff who work closely with the person with the dementia. This process requires a lot of effort with continuous observation of the person with dementia over the long term. This article investigates the effectiveness of using a straightforward method, based on a single wristband sensor to classify events of “Stressed” and “Not stressed” for people with dementia. The presented system calculates the stress level as an integer value from zero to five, providing clinical information of behavioral patterns to the clinical staff. Thirty staff members participated in this experiment, together with six residents suffering from dementia, from two nursing homes. The residents were equipped with the wristband sensor during the day, and the staff were writing observation notes during the experiment to serve as ground truth. Experimental evaluation showed relationships between staff observations and sensor analysis, while stress level thresholds adjusted to each individual can serve different scenarios.

User-Driven Ontology Population from Linked Data Sources

In order for ontology-based applications to be deployed in real-life scenarios, significant volumes of data are required to populate the underlying models. Populating ontologies manually is a time-consuming and error-prone task and, thus, research has shifted its attention to automatic ontology population methodologies. However, the majority of the proposed approaches and tools focus on analysing natural language text and often neglect other more appropriate sources of information, such as the already structured and semantically rich sets of Linked Data. The paper presents PROPheT, a novel ontology population tool for retrieving instances from Linked Data sources and subsequently inserting them into an OWL ontology. The tool, to the best of our knowledge, offers entirely novel ontology population functionality to a great extent and has already been positively received according to user evaluation.

The Tomaco Hybrid Matching Framework for SAWSDL Semantic Web Services

This work aims to advance Web Service retrieval, also known as Matching, in two directions. First, it introduces a matching algorithm for SAWSDL, which adapts and extends known concepts with novel strategies. Effective logic-based and syntactic strategies are introduced and combined in a novel hybrid strategy, targeting an envisioned well-defined, real-world scenario for matching. The algorithm is evaluated in a universal environment for matching algorithms, SME2, in an objective, reproducible manner. Evaluation ranks Tomaco high amongst state of the art, especially for early recall levels (first in macro-averaging precision, up to 0.7 recall). Secondly, this work introduces the Tomaco web application, which aims to promote wide-spread adoption of Semantic Web Services while targeting the lack of user-friendly applications in this field, by integrating a variety of configurable matching algorithms proposed in this paper. It, finally, allows discovery of both existing and user-contributed service collections and ontologies, serving also as a service registry.

Dem@Home: ambient intelligence for clinical support of people living with dementia

With the ever-growing prevalence of dementia, nursing costs are increasing, while the ability to live independently vanishes. Dem@Home is an ambient assisted living framework to support independent living while receiving intelligent clinical care. Dem@Home integrates a variety of ambient and wearable sensors together with sophisticated, interdisciplinary methods of image and semantic analysis. Semantic Web technologies, such as OWL 2, are extensively employed to represent sensor observations and application domain specifics as well as to implement hybrid activity recognition and problem detection. Complete with tailored user interfaces, clinicians are provided with accurate monitoring of multiple life aspects, such as physical activity, sleep, complex daily tasks and clinical problems, leading to adaptive non-pharmaceutical interventions. The method has been already validated for both recognition performance and improvement on a clinical level, in four home pilots.

VERGE in VBS 2017

This paper presents VERGE interactive video retrieval engine, which is capable of browsing and searching into video content. The system integrates several content-based analysis and retrieval modules including concept detection, clustering, visual similarity search, object-based search, query analysis and multimodal and temporal fusion.

The SemaDrift Protégé Plugin to Measure Semantic Drift in Ontologies: Lessons Learned

Semantic drift is an active research field, which aims to identify and measure changes in ontologies across time and versions. Yet, only few practical methods have emerged that are directly applicable to Semantic Web constructs, while the lack of relevant applications and tools is even greater. This paper presents the findings, current limitations and lessons learned throughout the development and the application of a novel software tool, developed in the context of the PERICLES FP7 project, which integrates currently investigated methods, such as text and structural similarity, into the popular ontology authoring platform, Protege. The graphical user interface provides knowledge engineers and domain experts with access to methods and results without prior programming knowledge. Its applicability and usefulness are validated through two proof-of-concept scenarios in the domains of Web Services and Digital Preservation; especially the latter is a field where such long-term insights are crucial.

Real-time health monitoring and contextualised alerts using wearables

This paper presents the HealthMon framework towards accessible mobile health. Following the emergence of wearables with rich sensing capabilities in the market, HealthMon repurposes an affordable, retail wristband to clinical monitoring scenarios for conditions that impair independence e.g. dementia, Parkinsons or ageing. Multiple sensor modalities, such as physical activity levels, posture and heart rate, are unanimously stored and interpreted to produce real-time alerts, using Semantic Web technologies. HealthMons constant monitoring capabilities are available to end-users and informal carers e.g. family and medical doctors alike, through mobile and web applications. The framework focuses on adoptability and deployability, receiving positive user feedback.

Flood Relevance Estimation from Visual and Textual Content in Social Media Streams.

Disaster monitoring based on social media posts has raised a lot of interest in the domain of computer science the last decade, mainly due to the wide area of applications in public safety and security and due to the pervasiveness not solely on daily communication but also in life-threating situations. Social media can be used as a valuable source for producing early warnings of eminent disasters. This paper presents a framework to analyse social media multimodal content, in order to decide if the content is relevant to flooding. This is very important since it enhances the crisis situational awareness and supports various crisis management procedures such as preparedness. Evaluation on a benchmark dataset shows very good performance in both text and image classification modules.

A Topic Detection and Visualisation System on Social Media Posts

Large amounts of social media posts are produced on a daily basis and monitoring all of them is a challenging task. In this direction we demonstrate a topic detection and visualisation tool in Twitter data, which filters Twitter posts by topic or keyword, in two different languages; German and Turkish. The system is based on state-of-the-art news clustering methods and the tool has been created to handle streams of recent news information in a fast and user-friendly way. The user interface and user-system interaction examples are presented in detail.

Dem@Home: Ambient Monitoring and Clinical Support for People Living with Dementia

Dem@Home is an Ambient Assisted Living framework to support intelligent dementia care, by integrating a variety of ambient and wearable sensors together with sophisticated, interdisciplinary methods, such as image and semantic analysis. Semantic Web technologies, such as OWL, are used to represent sensor observations and application domain specifics as well as to implement hybrid activity recognition and problem detection solutions. Complete with tailored user interfaces, Dem@Home supports accurate monitoring of multiple aspects, such as physical activity, sleep, complex daily activities and problems, leading to adaptive interventions for the optimal care of dementia, validated in four home pilots.