@inproceedings{salous_putze_2018esann,
  author={Salous, Mazen and Putze, Felix},
  title={Behaviour-Based Working Memory Capacity Classification Using Recurrent Neural Networks},
  booktitle={{ESANN} 2018 -- 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning},
  isbn = {978-2-87587-047-6},
  pages = {159--164},
  numpages = {6},
  url={https://www.csl.uni-bremen.de/cms/images/documents/publications/salous_putze_2018esann.pdf},
  address = {Brugge, Belgium},
  year={2018},
  abstract={A user's working memory capacity is a crucial factor for successful Human Computer Interaction (HCI). While reliable tests for working memory capacity are available, they are time-consuming, stressful, and not well-integrated into HCI applications. This paper presents a classifier based on Long Short Term Memory networks to exploit sparse temporal dependencies in behavioural data, collected in a complex, memory-intense interaction task, to classify working memory capacity. A cognitive user simulation is introduced to generate additional training data episodes that follow the behaviour of existing real data. We show that the classifier outperforms a linear baseline especially for short segments of data.},
}