@inproceedings{salous2022smarthelm,
  title={SmartHelm: User Studies from Lab to Field for Attention Modeling},
  author={Salous, Mazen and K\"{u}ster, Dennis and Scheck, Kevin and Dikfidan, Aytac and Neumann, Tim and Putze, Felix and Schultz, Tanja},
  booktitle={2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  pages={xx},
  year={2022},
  organization={IEEE},
  url={https://www.csl.uni-bremen.de/cms/images/documents/publications/salous2022smarthelm.pdf},
  abstract={We present three user studies that gradually prepare our prototype system SmartHelm for use in the field, i.e. supporting cargo cyclists on public roads for cargo delivery. SmartHelm is an attention-sensitive smart helmet that integrates none-invasive brain and eye activity detection with hands-free Augmented Reality (AR) components in a speech-enabled outdoor assistance system. The described studies systematically increased in ecological validity from lab to field. The first study consisted of an Augmented Reality preparation examination in the lab. The second study then investigated simulated attention distraction modeling, whereas the third study examined real world attention distraction modeling while cycling in traffic. During these three studies, multimodal data (EEG, eye-tracking, video, GPS and speech) has been collected synchronously and analyzed in offline and online experiments. Machine Learning models were trained and optimized for attention modeling. Results: Analyses of self-report and objective data during the simulation study show the plausibility of the simulated internal and external distractions. The analysis of behavioral data captured by multimodal biosignals recorded in the field study further shows that real visual attention distractions can be automatically identified using synchronized video and eye tracking data. Machine Learning methods based on long short term memory models (LSTMs) indicate that simulated attention distractions can be automatically detected from EEG data, with the best detection performance for mental distractions. Finally, the self-report data suggest that the comfort of the SmartHelm helmet should be further improved for permanent use in road traffic.}
}