LIBPHYS BIOSIGNALS

Instrumentation | Processing | Decision Making

OUR RESEARCH FOCUS

Instrument

We fabricate and use instrumentation tools for gathering signal data.

Process

As we acquire data we process it in order to have meaningful information.

Decide

As we find patterns in the data we give great insights on decision making.

Our group is focused in the area of biosignals. The main topics of our research are instrumentation, signal processing, and decision making applied to the medical field.


As for the area of instrumentation we use and fabricate tools for acquiring biological signals and we measure these biological systems we process them in order to find meaningful information about the person’s condition, state, and/or development. As we collect this data and use intelligent algorithms to detect patterns, we produce mechanisms that help in the decision on the action to be made in the face of the events that the acquired data is present.


Based on on this research, we deliver creative solutions to be applied in the health environment with the goal of improving people’s everyday lives.

TEAM

COORDINATOR

Hugo Gamboa

Associated Professor

RESEARCHERS

Luís Silva

Post-Doc Researcher

Phillip Probst

PhD Student – MIT Portugal

Mariana Dias

PhD Student – MIT Portugal

Dania Furk

Researcher

Inês Silveira

Researcher

Rodrigo Braga

Researcher

PUBLICATIONS

TOOLS

TSSEARCH: Time Series Subsequence Search Library

Go to publication here.

Go to toolbox here.

TSFEL: Time Series Feature Extraction Library

Go to publication here.

Go to toolbox here.

PROJECTS

Ongoing Projects

AISym4Med aims at developing a platform that will provide healthcare data engineers, practitioners, and researchers access to a trustworthy dataset system augmented with controlled data synthesis for experimentation and modeling purposes. This platform will address data privacy and security by combining new anonymization techniques, attribute-based privacy measures, and trustworthy tracking systems.

KEEPCARING focuses on enhancing the wellbeing and resilience of healthcare workers in EU hospitals along the surgical pathway. The initiative aims to improve recruitment and retention by systematically researching the causes and indicators of job stress and developing innovative solutions to mitigate these issues. This is achieved through the collaborative creation of a comprehensive package that integrates non-digital, digital, and AI-supported tools, designed to prevent burnout among both current and aspiring healthcare professionals at the individual, team, and organizational levels.

Finished Projects

The PrevOccupAI aimed to identify occupational risks by combining the power of historical records of the global population and the precision data of personalized occupational exposure records. The combination was achieved through advanced machine learning and AI techniques. The project was carried out by an interdisciplinary team of experts in biomedical data acquisition, advanced data processing, AI algorithms, epidemiology, and clinical validation.

The project aimed to approach the Industry 4.0 workplace holistically, but from the point of view of the workers, gathering and crossing quantitative and qualitative data, building a richer picture of the workers’ well-being and enabling better predictive models. We continuously validated our solutions and built large datasets through continuous fieldwork, ranging from short contact sessions during technical development to long-term trials enabling impact assessment.

COLLABORATIONS

CONTACT US

BIOSIGNALS.LIBPHYS@GMAIL.COM