IVLING - Virtual Sign Language Interpreter
One of the main challenges faced by the Deaf community is the communication barrier between deaf and hearing people, as knowledge of Sign Language (SL) among the hearing population remains limited.
Today, advances in technology and the emergence of new paradigms related to artificial intelligence — particularly Machine Learning and Deep Learning — as well as computer graphics and virtual reality, are making it possible to develop systems that improve access to public services and contribute to the effective inclusion of deaf people in society.
Within this context, the IVLinG Project aims to develop a digital platform for virtual, bidirectional Sign Language interpretation, streamlining communication between the Deaf community and hearing individuals. At the core of the solution is a real-time virtual interpreter of Portuguese Sign Language (LGP), capable of automatically recognising gestures, facial expressions, and body movements. These movements are translated into text and/or audio, allowing hearing users to receive the information directly on a computer or mobile device. Responses in LGP are presented through a three-dimensional avatar.
The project aims to create a versatile system that can be used on mobile devices or dedicated stations equipped with cameras, without requiring gloves or wearable motion-capture accessories
Co-financed by:
HEPIC COVID-19
This project builds on Hepic, which enables the control and monitoring of the entire epidemiological surveillance process for Healthcare-Associated Infections (HAIs).
Its main objective is to provide healthcare professionals with integrated access to patients’ clinical information related to infection or suspected infection episodes, including clinical records, microbiology laboratory data, antimicrobial prescription and dispensing, and emergency care information. This enables real-time analysis of each patient’s clinical history, supports therapeutic decision-making, and facilitates the timely identification and control of potential sources of hospital infection.
The aim of this project is to extend the Hepic environment through the development of a new regional solution that leverages the information generated by local Hepic® systems. The solution will support real-time regional reporting and monitoring of the evolution of COVID-19-related conditions through systems integration and data sharing.
Artificial Intelligence (AI) mechanisms will be used to correlate patient data based on demographic information, clinical history, and disease progression.
The system is designed to provide aggregated information to support operational decision-making by regional healthcare management bodies in the context of pandemic events.
It may also become a valuable tool for scientific research, as it includes functionality for exporting structured anonymised data.
Co-financed by:
TAMI – Transparent Artificial Medical Intelligence
The TAMI project, developed through a consortium between First Solutions, INESC TEC, Fraunhofer Portugal Research, and the Northern Regional Health Administration, with the participation of researchers from Carnegie Mellon University, aims to create a new platform based on Artificial Intelligence methods capable of delivering medical test results while simultaneously providing explainability regarding how those results were obtained.
The project focuses on the study of glaucoma, cervical pathologies, and thoracic pathologies. The platform is intended for academic, scientific, and commercial use, supporting healthcare professionals in clinical decision-making.
The project is co-financed under the Incentive System for Research and Technological Development, integrating the Interface Programme.
Co-financed by:
Eye Fundus Scope NEO
The EyeFundusScopeNEO project, developed through a consortium between CUF Infante Santo Hospital, Fraunhofer Portugal Research, and First Solutions – Sistemas de Informação S.A., aims to optimise, demonstrate, and evaluate a diabetic retinopathy screening solution designed for healthcare professionals without specialist training in ophthalmology.
The solution integrates the EyeFundusScope retinal image acquisition system, developed by Fraunhofer AICOS, with the healthcare management information system developed by First Solutions. This integration enables non-specialist healthcare professionals to capture retinal images and make them available to ophthalmologists for clinical evaluation of diabetic retinopathy.
Within the context of CUF Infante Santo Hospital, the EyeFundusScope will be optimised for usability by non-specialists and integrated with First Solutions’ diabetic retinopathy assessment information system, adapted to meet the project’s objectives. Retinal images acquired during consultations and community outreach initiatives will be analysed by CUF ophthalmologists, who will ensure appropriate follow-up whenever signs of disease are identified. The project includes two pilot implementations at CUF Infante Santo Hospital.
Project co-financed under the Incentive for Business R&D in the Demonstrator Projects in Co-promotion category.
Co-financed by:
FIRST joins Fraunhofer Portugal AICOS to improve diabetic retinopathy screening
EyeFundusScope, an award-winning project developed by Fraunhofer Portugal AICOS, is a key component of the Mobile Diabetic Retinopathy Screening project (MobileDRS), led by the Portuguese company First Solutions S.A. The project aims to integrate and validate a mobile solution for diabetic retinopathy risk assessment within a commercial platform for the management of population screening programmes.
Diabetic retinopathy is a complication of diabetes and remains one of the leading causes of preventable blindness worldwide. Continuous monitoring is essential to preserve vision, as the disease often progresses without symptoms in its early stages. However, the limited mobility of traditional retinal imaging equipment and the shortage of specialist professionals reduce the effectiveness and accessibility of screening programmes.
The MobileDRS project includes the adaptation of the EyeFundusScope mobile prototype, previously recognised with an award by Exame Informática. The solution combines smartphone-based retinal image acquisition with 3D-printed components and a dedicated optical system, enabling retinal images to be captured in a flexible, affordable, and user-friendly way by a broader range of healthcare professionals, including non-specialists.
Following validation, the MobileDRS solution is expected to play an important role in expanding screening coverage to isolated and underserved populations through the use of a low-cost mobile device. This approach has the potential to reduce healthcare costs while increasing the effectiveness of diabetic retinopathy screening programmes.
The mobile solution will be integrated into SiiMA Rastreios, an information system for the management of population screening programmes that supports the entire workflow, from screening invitation and examination to treatment and follow-up.
The project (POCI-01-0247-FEDER-010838), under Portugal 2020, is co-funded by the European Structural and Investment Funds (ESIF) of the European Union within the scope of COMPETE 2020 – Operational Programme for Competitiveness and Internationalisation.
Co-financed by:
