Abstract
Qualitative data collected from International Space Station (ISS) postflight crew debriefs was used to evaluate the performance of a convolutional neural network (ConvNet) model. While the ISS postflight debriefs cover a broad range of spaceflight and on-orbit operations related topics, this model was specifically trained and tested to classify debrief comments as safety related or not, based on a previously coded subset of debrief comments that were manually evaluated by human factors engineers to determine if a comment had safety implications. This evaluation revealed that a ConvNet can adequately determine whether textual debrief comments contain safety data. These methods can potentially save large amounts of manual effort on the part of human factors engineers and improve the ability to identify and act on crew knowledge that informs or identifies risk to spaceflight crew.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bryant, C., Schuh, S., Schoenstein, N., Meza, D.: Understanding the international space station crew perspective following long duration missions through data analytics and visualization of crew feedback. In: Ahram, T., Falcão, C. (eds.) Advances in Usability and User Experience, AHFE 2017. Advances in Intelligent Systems and Computing, vol. 607. Springer, Cham (2018)
DL4J: Introduction to deep neural networks (deep learning) - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM. https://deeplearning4j.org/neuralnet-overview
Swietojanski, P., Ghoshal, A., Renals, S.: Convolutional neural networks for distant speech recognition. IEEE Sig. Process. Lett. 21(9), 1120–1124 (2014). https://doi.org/10.1109/lsp.2014.2325781
Georgakopoulos, S.V., Tasoulis, S.K., Vrahatis, A.G., Plagianakos, V.P.: Convolutional neural networks for toxic comment classification. Cornell University Library (2018). https://arxiv.org/pdf/1802.09957.pdf
Thomas, S., Ganapathy, S., Saon, G., Soltau, H.: Analyzing convolutional neural networks for speech activity detection in mismatched acoustic conditions. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2014). https://doi.org/10.1109/icassp.2014.6854054
Gutierrez-Osuna, R.: Introduction to Speech Processing. http://research.cs.tamu.edu/prism/lectures/sp/l15.pdf
Kim, Y.: Convolutional neural network for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751 (2014)
Comfort, S., Perera S., Hudson, Z., Dorrell D., Meireis S., Nagarajan M., Ramakrishnan C., Fine, J.: Sorting through the safety data haystack: using machine learning to identify individual case safety reports in social-digital media. Drug Saf. 41, 579–590 (2018)
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2018). http://www.R-project.org/
RStudio Team: RStudio: Integrated Development for R. RStudio, Inc., Boston, MA (2018). http://www.rstudio.com/
Henry, L., Wickham, H.: purrr: Functional Programming Tools. R package version 0.2.4. (2017). https://CRAN.R-project.org/package=purrr
Dragulescu, A.: xlsx: Read, write, format Excel 2007 and Excel 97/2000/XP/2003 files. R package version 0.5.7. (2014). https://CRAN.R-project.org/package=xlsx
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
TensorFlow Team: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). tensorflow.org
Mullen, L.: tokenizers: A Consistent Interface to Tokenize Natural Language Text. R package version 0.1.4. (2016). https://CRAN.R-project.org/package=tokenizers
Selivanov, D., Wang, Q.: text2vec: Modern Text Mining Framework for R. R package version 0.5.1. (2018). https://CRAN.R-project.org/package=text2vec
Chollet, F., et al.: Keras, GitHub (2018). https://github.com/fchollet/keras
Gagolewski, M., et al.: R package stringi: Character string processing facilities (2017). http://www.gagolewski.com/software/stringi/. https://doi.org/10.5281/zenodo.32557
Chollet, F., Allaire, J.: Keras: R Interface to ‘Keras’. R package version 2.1.4. (2018). https://CRAN.R-project.org/package=keras
Kuhn, M., Contributions from Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A., Cooper, T., Mayer, Z., Kenkel, B.: The R Core Team, Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L., Tang, Y., Candan, C., Hunt, T.: Caret: Classification and Regression Training. R package version 6.0-78 (2017). https://CRAN.R-project.org/package=caret
Acknowledgments
The authors thank Katherine Vasser of MEI Technologies (NASA JSC contractor) and Rhonda Russo of JES Tech (NASA JSC contractor) for their expertise and assistance with collection and processing of the ISS Crew Comments data. The authors also thank Laura Duvall and John McBrine of NASA JSC for their guidance and support concerning the Crew Comments data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Bryant, C., Schoenstein, N., Schuh, S., Meza, D. (2019). Comparing Automated vs. Manual Data Analytic Processing of Long Duration International Space Station Post Mission Crew Feedback. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2018. Advances in Intelligent Systems and Computing, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-93885-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-93885-1_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93884-4
Online ISBN: 978-3-319-93885-1
eBook Packages: EngineeringEngineering (R0)