The capability to recognize human emotions plays a significang role in applications ranging from human computer interaction and entertainment to psychology and education. In CMVS we believe that combining complementary information from different modalities increases the accuracy of emotion recognition.

Low-energy computing requires that the power demands are on par with energy that can be harvested from the environment. In practice, only a few mW may be available for sensing, communications, and computing. In this domain we investigate energy efficient fully programmable architectures and high-level design tool chains.

Individualised healthcare is a recent global megatrend aiming to improve health and wellbeing. We are developing breakthrough technologies to tackle key challenges including next generation signal analysis techniques towards personalized medicine and wellness solutions.

In recent years, increasing resolving power and automation of biomedical imaging systems have resulted in an exponential growth of the image data. Manual analysis of these data sets is extremely labor intensive and hampers the objectivity and reproducibility of results. Hence, there is a growing need for automatic image processing and analysis methods. In CMVS, our aim has been to apply modern computer vision techniques to biomedical image analysis which is one of our emerging research areas.