Comparative Analysis of Think-aloud Methods for Everyday Activities in the Context of Cognitive Robotics
by , , ,
Abstract:
We describe our efforts to compare data collection methods using two think-aloud protocols in preparation to be used as a basis for automatic structuring and labeling of a large database of high-dimensional human activities data into a valuable resource for research in cognitive robotics. The envisioned dataset, currently in development, will contain synchronously recorded multimodal data, including audio, video, and biosignals (eye-tracking, motion-tracking, muscle and brain activity) from about 100 participants performing everyday activities while describing their task through use of think-aloud protocols. This paper provides details of our pilot recordings in the well-established and scalable ``table setting scenario," describes the concurrent and retrospective think-aloud protocols used, the methods used to analyze them, and compares their potential impact on the data collected as well as the automatic data segmentation and structuring process.
Reference:
Comparative Analysis of Think-aloud Methods for Everyday Activities in the Context of Cognitive Robotics (Moritz Meier, Celeste Mason, Felix Putze, Tanja Schultz), In 20th Annual Conference of the International Speech Communication Association, 2019. (Interspeech 2019)
Bibtex Entry:
@inproceedings{meier_interspeech_2019,
   note={Interspeech 2019},
   title={Comparative Analysis of Think-aloud Methods for Everyday Activities in the Context of Cognitive Robotics},
   year={2019},
   month={October},
   booktitle={20th Annual Conference of the International Speech Communication Association},
   address={Graz, Austria},
   author={Meier, Moritz and Mason, Celeste and Putze, Felix and Schultz, Tanja},
   url={https://www.csl.uni-bremen.de/cms/images/documents/publications/meier_interspeech_2019.pdf},
   abstract={We describe our efforts to compare data collection methods using two think-aloud protocols in preparation to be used as a basis for automatic structuring and labeling of a large database of high-dimensional human activities data into a valuable resource for research in cognitive robotics. The envisioned dataset, currently in development, will contain synchronously recorded multimodal data, including audio, video, and biosignals (eye-tracking, motion-tracking, muscle and brain activity) from about 100 participants performing everyday activities while describing their task through use of think-aloud protocols. This paper provides details of our pilot recordings in the well-established and scalable ``table setting scenario," describes the concurrent and retrospective think-aloud protocols used, the methods used to analyze them, and compares their potential impact on the data collected as well as the automatic data segmentation and structuring process.}
}