Capturing Interaction Quality in Long Duration (Simulated) Space Missions with Wearables

Journal Article (2022)
Authors

E. Gedik (TU Delft - Pattern Recognition and Bioinformatics)

Jeffrey Olenick (Michigan State University)

Chu-Hsiang Chang (Michigan State University)

Steve W.J. Kozlowski (Michigan State University)

H.S. Hung (TU Delft - Pattern Recognition and Bioinformatics)

Research Group
Pattern Recognition and Bioinformatics
Copyright
© 2022 E. Gedik, Jeffrey Olenick, Chu-Hsiang Chang, Steve W.J. Kozlowski, H.S. Hung
To reference this document use:
https://doi.org/10.1109/TAFFC.2022.3176967
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 E. Gedik, Jeffrey Olenick, Chu-Hsiang Chang, Steve W.J. Kozlowski, H.S. Hung
Research Group
Pattern Recognition and Bioinformatics
Issue number
3
Volume number
14
Pages (from-to)
2139-2152
DOI:
https://doi.org/10.1109/TAFFC.2022.3176967
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Abstract

Space exploration is evolving with the recent increase in interest and investment. For the success of planned long-duration crewed missions, good interpersonal interactions between crew members are crucial. In this study, we evaluate the use of wearables for detection and estimation of the quality of each social interaction participants have throughout a long mission rather than aggregate measures of interactions. Our proposed method utilizes Temporal Convolutional Networks(TCNs) for extracting individual representations from acceleration and audio streams and learnable pooling layers(NetVLAD) to aggregate these representations into fixed-size representations. Use of NetVLAD layers provides an intelligent alternative to simple aggregation for handling variable-sized interactions and interactions with missing data. We evaluate our method on a 4-month simulated space mission where 5 participants wore Sociometric Badges and provided reports on their interactions in terms of effectiveness, frustration, and satisfaction. Our method provides an average ROC-AUC score of 0.64. Since we are not aware of any comparable baselines, we compare our method to hand-crafted features formerly utilized for cohesion estimation in similar scenarios and show it significantly outperforms them. We also present ablation studies where we replace the components in our approach with well-known alternatives and show that they provide better performance than their respective counterparts.

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