Sensor Technology Research Centre

Research: HumanE AI Net

The HumanE AI Net project is EU H2020 funded project running 2020-2023. With 12M€ funding and 53 partners, this project aims to advance AI for the societal good.

The project vision is as follows.

The HumanE AI Network will leverage the synergies between the involved centers of excellence to develop the scientific foundations and technological breakthroughs needed to shape the AI revolution in a direction that is beneficial to humans both individually and societally, and that adheres to European ethical values and social, cultural, legal, and political norms. The core challenge is the development of robust, trustworthy AI systems capable of what could be described as “understanding” humans, adapting to complex real-world environments, and appropriately interacting in complex social settings. The aim is to facilitate AI systems that enhance human capabilities and empower individuals and society as a whole while respecting human autonomy and self-determination. The HumanE AI Net project will engender the mobilization of a research landscape far beyond direct project funding, involve and engage European industry, reach out to relevant social stakeholders, and create a unique innovation ecosystem that provides a manyfold return on investment for the European economy and society.

The Wearable Technologies Lab at the University of Sussex is primarily active within the workpackage Multi-modal perception and learning. This workpackage is described as follows:

Our ambition it to build on recent progress in discriminative and generative networks, to provide integrated multi-modal perception and modeling that combines fast real-time reaction for sensori-motor reflexes, with spatiotemporal and geometric reasoning, prediction of recurrent events and consequences for actions and dynamic processes and linguistic expressions for perceptual concepts to enable communication with and learning from humans. In prticuar we intend to develop systems that can understand complex human actions, motivations and social settings.

Within the project, our group will lead activities to:

  • Federate datasets related to human activity sensing, notably creating or identifying "benchmark datasets".
  • Organise challenges in computational behavioral analytics with AI and sensors
  • Seek to identify principles, methodologies, guidelines or checklists to assess ethical characteristics of computational behaviour science.


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