Runtime: 24 months

Over the last years, imaging photoplethysmography (iPPG) has been attracting immense interest. iPPG assesses the cutaneous perfusion by exploiting subtle color variations from videos. Common procedures use RGB cameras and employ the green channel or rely on a linear combination of RGB to extract physiological information. iPPG can capture multiple parameters such as heart rate (HR), heart rate variability (HRV), oxygen saturation, blood pressure, venous pulsation and strength as well as spatial distribution of cutaneous perfusion. Its highly convenient usage and a wide range of possible applications, e.g. patient monitoring, using skin perfusion as early risk score and assessment of lesions, make iPPG a diagnostic mean with immense potential. Under real -world conditions, however, iPPG is prone to errors. Particularly regarding analyses beyond HR, the number of published works is limited, proposed algorithms are immature, basic mechanisms are not completely understood and iPPG’s potential is far from being exploited. We hypothesize that hyperspectral (HS) reconstruction by artificial intelligence (AI) methods can fundamentally improve iPPG and extend its applicability. HS reconstruction refers to the estimation of HS images from RGB images. The technique has recently gained much attention but is not common to iPPG. COHYPERA aims to prove the potential of HS reconstruction as universal processing step for iPPG. The pursued approach takes advantage of the fact that the HS reconstruction can incorporate knowledge and training data to yield a high dimensional data representation, which enables various analyses.

Noise Embeddings within a Hearing Aid Tailored Deep Learning  Noise Suppression Framework

Runtime: 12 Months

Partner: Sivantos GmbH, Erlangen

The overall goal of this project is to develop a Noise Suppression Framework for hearing aids, which can be extended by so called “embeddings” to allow a certain modification of the noise reduction behavior without re-training of the overall system. In doing so, typical hearing aid requirements like the preservation of the desired speech, the overall delay of the system, and certain aspects of flexible parameterization (e.g. with respect to the amount of noise reduction should be considered.

SHIFT: MetamorphoSis of cultural Heritage Into augmented hypermedia assets For enhanced accessibiliTy and inclusion (#101060660)

EU Horizon 2020 Research & Innovation Action (RIA)

Runtime: 01.10.2022 – 30.09.2025

Partners:  Software Imagination & Vision, Foundation for Research and Technology, Massive Dynamic, Audeering, University of Augsburg, Queen Mary University of London, Magyar Nemzeti Múzeum – Semmelweis Orvostörténeti Múzeum, The National Association of Public Librarians and Libraries in Romania, Staatliche Museen zu Berlin – Preußischer Kulturbesitz, The Balkan Museum Network, Initiative For Heritage Conservation, Eticas Research and Consulting, German Federation of the Blind and Partially Sighted

The SHIFT project is strategically conceived to deliver a set of technological tools, loosely coupled that offers cultural heritage institutions the necessary impetus to stimulate growth, and embrace the latest innovations in artificial intelligence, machine learning, multi-modal data processing, digital content transformation methodologies, semantic representation, linguistic analysis of historical records, and the use of haptics interfaces to effectively and efficiently communicate new experiences to all citizens (including people with disabilities).

Hier finden Sie eine Übersicht über vergangene Projekte.