Publications

Below you will find a list of scientific and other publications that are direct results of the research done in the ALAMEDA project.

All publications are freely downloadable and disseminated in open access modalities

 

Published: 22 November 2022

An Affective Multi-Modal Conversational Agent for Non Intrusive Data Collection from Patients with Brain Diseases

Authors: C. Chira, E. Mathioudis, C. Michailidou, P. Agathangelou, G. Christodoulou, I. Katakis, E. Kontopoulos, and K. Avgerinakis

This paper presents Zenon, an affective, multi-modal conversational agent (chatbot) specifically designed for treatment of brain diseases like multiple sclerosis and stroke.

Zenon collects information from patients in a non-intrusive way and records user sentiment using two different modalities: text and video. A user-friendly interface is designed to meet users’ needs and achieve an efficient conversation flow. What makes Zenon unique is the support of multiple languages, the combination of two information sources for tracking sentiment, and the deployment of a semantic knowledge graph that ensures machine-interpretable information exchange.

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Published: 20 June 2022

Monitoring Neurological Disorder Patients via Deep Learning Based Facial Expressions Analysis

Authors: M. Munsif, M. Ullah, Bilal Ahmad, M. Sajjad, Faouzi Alaya Cheikh

Facial expression (FE) is the most natural and convincing source to communicate human emotions, providing valuable insides to the observer while assessing the emotional incongruities. In health care, the FE of the patient (specifically of neurological disorders (NDs) such as Parkinson’s, Stroke, and Alzheimer’s) can assist the medical doctor in evaluating the physical condition of a patient, such as fatigue, pain, and sadness.

Download pdf: full article (pp. 412-423)

Published: 29 June 2022

Balancing between holistic and cumulative sentiment classification

Authors: P. Agathangelou, I. Katakis

Our method, by combining convolutional, recurrent and attention neural networks can extract rich linguistic patterns that reveal the user’s sentiment towards the entity under review. We evaluate our method in nine datasets that represent both binary and multi-class classification tasks.

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Published: 13 May 2022

Usability of the Virtual Supermarket Test for Older Adults with and without Cognitive Impairment

Authors: S. Zygourisa, S. Segkoulia, A. Triantafyllidisa, D. Giakoumisa

This study conducted a preliminary usability assessment of the Virtual Supermarket Test (VST), a serious gamebased self-administered cognitive screening test for mild cognitive impairment (MCI).

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Published: 1 April 2022

A Semantic Technologies Toolkit for Bridging Early Diagnosis and Treatment in Brain Diseases: Report from the Ongoing EU-Funded Research Project ALAMEDA

Authors: C. Maga-Nteve, E. Kontopoulos, N.Tsolakis, I. Katakis, E. Mathioudis, P. Mitzias, K. Avgerinakis, G. Meditskos, A. Karakostas, S. Vrochidis, I. Kompatsiaris 

Semantic Web technologies are increasingly being deployed in various e-health scenarios, prominently due to their inherent capacity to harmonize heterogeneous information from diverse sources and devices, as well as their capability to provide meaningful interpretations and higher-level insights. This paper reports on ongoing work in the recently started EU-funded project ALAMEDA towards a semantic toolkit for bridging the gap between early diagnosis and treatment in a variety of brain diseases.

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Published: 24 February 2022

Internet of Things Technologies and Machine Learning Methods for Parkinson’s Disease Diagnosis, Monitoring and Management: A Systematic Review

Authors: K-M. Giannakopoulou, I. Roussaki, K. Demestichas

This paper presents a state-of-the-art systematic review of the recent advances in the Internet of Things and Artificial Intelligence applications on Parkinson’s Disease, including the subdomains of machine learning and deep learning.

(This article belongs to the Special Issue AI and IoT Enabled Solutions for Healthcare)

 

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Published: 22 December 2022

Ontology based semantic model for health data interpretation

Authors: C. Maga-Nteve, N. Tsolakis, G. Meditskos, A. Karakostas, S. Vrochidis, I. Kompatsiaris

In this paper we propose a new semantic data model in which health information derived from Parkinson’s, Multiple Sclerosis, and Stroke (PMSS) patients is systematically analyzed to generate and improve knowledge that will be transferred to patient care in order to design and develop innovative health risk prediction and intervention tools

 

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