Intelligent Systems

The Intelligent Systems group is focused on building solutions for business fields where intelligent ICT is emerging. It is composed of researchers with in-depth expertise in areas such as Signal & Image Processing, Artificial Intelligence and Cognitive Computing: the nuclear areas required to develop systems with the ability to perceive, analyse, learn and ultimately reason about the environments they operate in.

The Intelligent Systems group is divided in four sub-areas, which map our core competences:


/ Edge and cloud Computer Vision

We develop edge and cloud computer vision applications not only for image acquisition and processing, but also for identifying the relationship between multiple objects in images or videos. Our team is skilled in the development of portable and affordable image acquisition devices using advanced techniques that culminate in a better understanding of visual contents, which represents a quantum leap in several application areas.

The team is focused on mobile and embedded technology, which facilitate the creation of field-portable, cost-effective imaging and sensing technologies that are approaching laboratory-grade instrument performance. Additionally, we have been developing and optimising advanced algorithms while considering the technological constraints of edge devices, such as small-form IoT, wearables, mobile phones, and embedded processors. 


/ Sensor fusion & Embedded intelligence

Information-gathering devices and sensors are now ubiquitous due to the explosive growth of smart devices and the emergence of the Internet of Things (IoT). We at Fraunhofer AICOS use sensor data fusion to integrate multiple data representing the same real world object into a consistent, accurate and useful representation.

Our team came to specialize in higher-levels of data fusion, for instance, through the application of advanced machine learning and statistical processes. By doing this, we maximise the value of data coming from multiple sources, and altogether, we support intelligent system behaviors. By fusing sensors, along with embedded processing and connectivity, we enable context awareness and a plethora of new services for the benefit of society. 


/ Cognitive systems and Deep learning 

At Fraunhofer AICOS, we develop cognitive systems inspired in the way the human brain works as well as its capabilities to learn from past experiences to solve highly complex tasks.

We research different information extraction techniques such as text and natural language processing, computer vision and signal processing, to extract information from unstructured sources of information. We combine and integrate multiple sources of information, so that our cognitive systems can understand the meaning and the relation between relevant concepts in any context.

We are also committed to addressing the legal compliance from the initial stages of development, to ensure that dataset bias and unfairness are minimised, and that the transparency of machine-decisions as well as that our solutions have a positive impact in the world.  


/ Predictive modelling & Recommendation 

Intelligent systems based on predictive modelling use data to assess certain conditions over time and, based on previous events, model their course and predict future states. At Fraunhofer AICOS, we build predictive modelling and recommendation based on intelligent systems, that are robust and reliable, but also transparent, self-explanatory and easily interpretable for humans.

We focus on computer-aided detection and decision support systems to assist professionals in their routines, for example, early diagnosis and screening, by integrating their knowledge into computer systems.   

Highlighted Projects#


Captivating and challenging exergames
tailored for fall prevention, based on typical exergames, dance and Tai Chi, with the
objective of increasing physical activity of older adults.


A mobile solution and decision-support
system that aims to provide an indication of the presence of Diabetic Retinopathy.


A software package to support healthy
nutrition through all phases of ageing, from active seniors to elderly users in need of care, taking into account diet restrictions.

Further information#


Competence Articles


TSFEL: Time Series Feature Extraction Library


Relevant Services


Rapid Prototyping

Innovation Studies

Education & Training

Machine Learning Training


Relevant Publications


Felgueiras S., Costa J., Gonçalves J., & Soares F. (2018). Mobile-based Risk Assessment of Diabetic Retinopathy using a Smartphone and Adapted Ophtalmoscope. In Proceedings of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, 168-175. More info

Folgado D., Barandas M., Matias R., Martins R., Carvalho M. Â., & Gamboa H. (2018). Time Alignment Measurement for Time Series. In Pattern Recognition 81, 268-279. More info

Silva J., Sousa I., & Cardoso J. (2018). Transfer learning approach for fall detection with the FARSEEING real-world dataset and simulated falls. In 40th International Engineering in Medicine and Biology Conference (EMBC)

Rosado L., Costa J., Elias D., & Cardoso J. (2017). Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination. Sensors, 17. More info

Guimarães V., Castro L., Carneiro S., Monteiro M., Rocha T., Barandas M., Machado J., Vasconcelos M., Gamboa H., & Elias D. (2016). A motion tracking solution for indoor localization using smartphones. In 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). More info

Vasconcelos M.J.M., Rosado L., & Ferreira M. (2014). Principal Axes-based Asymmetry Assessment Methodology for Skin Lesion Image Analysis. In ISVC 2014: Advances in Visual Computing, 21-31. More info