Machine Learning
Machine Learning

Human Social Behavior Detection and Recognition

In this theme we apply and develop Machine Learning techniques for the detection and recognition of social signals and behaviors of humans in order develop and design social intelligent systems.

Recent publications

  • op den Akker, H.J.A. and Klaassen, R. and Nijholt, A. (2016) Virtual coaches for healthy lifestyle. In: Toward Robotic Socially Believable Behaving Systems - Volume II: Modeling Social Signals. Intelligent Systems Reference Library 106. Springer Verlag, Berlin, Germany, pp. 121-149. ISSN 1868-4394 ISBN 978-3-319-31052-7
  • Andujar, M. and Nijholt, A. and Gilbert, J.E. (2016) Designing a Humorous Workplace: Improving and Retaining Employee's Happiness. In: Proceedings of the AHFE 2016 International Conference on Affective and Pleasurable Design, 27-31 July 2016, Orlando, Florida. pp. 683-693. Advances in Intelligent Systems and Computing 483. Springer International Publishing. ISSN 2194-5357 ISBN 978-3-319-41660-1
  • Nijholt, A. (2016) Human Avatars in Playful and Humorous Environments. In: Proceedings of the AHFE 2016 International Conference on Affective and Pleasurable Design, 27-31 July 2016, Orlando, Florida. pp. 671-682. Advances in Intelligent Systems and Computing 483. Springer International Publishing. ISSN 2194-5357 ISBN 978-3-319-41660-1
  • Nijholt, A. and Poel, M. (2016) Multi-Brain BCI: Characteristics and Social Interactions. In: Proceedings of the 10th International Conference on Foundations of Augmented Cognition: Neuroergonomics and Operational Neuroscience, AC 2016, Held as Part of HCI International 2016, 17-22 July 2016, Toronto, Canada. pp. 79-90. Lecture Notes in Computer Science 9743. Springer Verlag. ISSN 0302-9743 ISBN 978-3-319-39954-6

Contact

For more information, please contact: Mannes Poel