It seems the ETRO members had fun at the PK Galabal

• Jan Cornelis becomes HoD.
• First Patent on an optical switch (IMOS) is filed.
• ETRO becomes co-founder of the International Postgraduate Course on Biomedical Engineering and Medical Physics (University of Patras) – Erasmus/Tempus.
• ETRO’s first EU framework project, BIOLAB: An Integrated Biomedical Laboratory – AIM- Advanced Informatics in Medicine.
• Data collection, drafting and validation of the first ever table of sizes for the Belgian apparel industry.

On February 20th 2025 at 16:00, Ángel Solé Morillo will defend their PhD entitled “M3-PPG: TOWARDS NOVEL (PERSONALIZED) PHOTOPLETHYSMOGRAPHY SYSTEMS THROUGH THE UNDERSTANING OF KEY INFLUENCING FACTORS”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

Photoplethysmography (PPG) is a low-cost technique that allows for extracting physiological parameters, such as heart rate or blood oxygen saturation, through light interactions with the skin. PPG has been present in clinical practice as the technology behind pulse oximeters since the 1980s. With the proliferation of health wearables equipped with PPG sensors in the last 15 years and the advancement in PPG applications beyond pulse oximetry, a new perspective has arisen, with PPG having the potential to tackle some key societal and health issues of the 21st century.

Despite its widespread adoption, PPG remains susceptible to various factors that can compromise the accuracy of the physiological measurements. Understanding these influences individually can improve and expand the use and applications of PPG, ultimately enabling personalized health monitoring.

This research first proposes a theoretical framework, which describes key hardware and software improvements that can enable robust and personalized physiological monitoring using PPG technology. Next, an analysis of the impact of the instrumentation on the PPG signal is presented.

In addition, the impact of skin tone on the PPG signal is evaluated. Melanin, another PPG influencing factor whose content in the skin gives rise to different skin colors, is measured with two prototypes in a pilot study. This contributes to addressing the oxygen saturation overestimation in pulse oximeters for users with darker skin tones.

The final part integrates all previous research findings into a prototype designed for continuous vital sign monitoring at the chest, whose performance was validated through an initial pilot study with healthy participants. This work also analyses how regulations impact this prototype’s possible road to market as a medical device in Europe and the U.S.

On February 1 2021 at 16.00 Tien Do Huu will defend his PhD entitled “A generic source coding. Methodology and architecture of dynamic holograms”.

Everybody is invited to attend the presentation online via https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2EwMTQ2ZmMtMjA2Ny00MGQwLTk2ZDctODNmMzE3NGEyNDMz%40thread.v2/0?context=%7b%22Tid%22%3a%22695b7ca8-2da8-4545-a2da-42d03784e585%22%2c%22Oid%22%3a%2237757df7-4050-47fe-8384-aded7e9e79c3%22%7d

We address the two challenges of big heterogeneous data originated from social media and smart cities, namely data quality enhancement and data exploitation. In the first challenge, we consider several subproblems commonly found in social media data and smart city data, including user location prediction, traffic data denoising, and hyperlocal air quality prediction. For the second challenge, we aim to gain insights from data, namely we focus on detecting fake news using social media data. As there exist correlations across datapoints in social media and smart city data, we propose exploiting these correlations using graph-based deep learning techniques to address the aforementioned challenges.

Our contributions are associated with the concerned subproblems. In user location prediction, we propose a novel deep multiview model combining multiple aspects of social media data. One of the inputs of the multiview model is node representation, which is learned using a graph-deep-learning-based technique. In order to denoise traffic data, we design a special graph auto-encoder with a Kron-reduction-based pooling scheme. We devise a graph variational auto-encoder in dealing with the air quality prediction problem. For fake news detection, we propose using a graph convolutional neural network, which captures the relation between articles shared by suspicious publishers. Having experienced graph neural networks on different applications, our last contribution focuses on a more fundamental problem of regularizing graph neural networks by introducing a novel regularization technique based on dropping nodes. We conduct comprehensive experiments on benchmark datasets to verify the effectiveness of the proposed methods. Finally yet importantly, although some of our methods are designed for specific problems (e.g., air pollution prediction), our formulations are general, leading to the possibility of using these methods for different applications (e.g., recommender systems) in related domains.

We are very proud of Brent De Weerdt (prom N. Deligiannis), Joris Wuts (prom. J. Vandemeulebroucke) and Silvia Zaccadi (prom. B. Jansen) who received their scholarship from FWO aspirant strategic basic research for the next 2+2 years. Way to go!

Fawaz Samani received the FWO Aspirant strategisch basisonderzoek mandate with the project “Towards Human Friendly Explanations”