Machine perception is oriented towards the study, design and development of intelligent perceptual systems for decision support in different application domains. Intelligent systems, using multi-sensory technologies, perceive and respond to the environment around them and interact with humans in complex and dynamic physical and social environments.

The methodological approach integrates artificial intelligence, machine learning, computer vision, data science and intelligent computational approaches to produce innovation in challenging application contexts such as: manufacturing, marine science, aerospace, robotics and automation, rail transport, ambient assisted living, agriculture, agri-food, personalised medicine, bioinformatics, drug design, innovative human-machine interaction, motor control analysis and electromyography, tracking and study of motor activities in laboratories and clinics.

Continuous learning from available data, knowledge formalisation, data-driven modelling, integration and interpretation of heterogeneous multisensory data, design of innovative human-computer interaction systems, design and management of sensor networks are examples of the challenges faced by research and the development of systems able to solve perception and decision making problems in complex real-world contexts with a high level of dynamism and uncertainty.