Academic Journal
Capturing Interaction Quality in Long Duration (Simulated) Space Missions with Wearables
| العنوان: | Capturing Interaction Quality in Long Duration (Simulated) Space Missions with Wearables |
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| المؤلفون: | Gedik, E. (author), Olenick, Jeffrey (author), Chang, Chu-Hsiang (author), Kozlowski, Steve W.J. (author), Hung, H.S. (author) |
| سنة النشر: | 2022 |
| المجموعة: | Delft University of Technology: Institutional Repository |
| مصطلحات موضوعية: | learnable pooling, long duration space missions-, missing data, social interactions, temporal convolutional networks, Wearable sensing |
| الوصف: | 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. ; Pattern Recognition and Bioinformatics |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| ردمك: | 978-85-13-07770-2 85-13-07770-4 |
| Relation: | http://www.scopus.com/inward/record.url?scp=85130777047&partnerID=8YFLogxK; IEEE Transactions on Affective Computing--1949-3045--8dd77b40-0007-4d98-bf72-03a9f8b94d88; http://resolver.tudelft.nl/uuid:0bbdf292-7097-4d7c-ae24-080b76257130; https://doi.org/10.1109/TAFFC.2022.3176967 |
| DOI: | 10.1109/TAFFC.2022.3176967 |
| الاتاحة: | http://resolver.tudelft.nl/uuid:0bbdf292-7097-4d7c-ae24-080b76257130 https://doi.org/10.1109/TAFFC.2022.3176967 |
| Rights: | © 2022 E. Gedik, Jeffrey Olenick, Chu-Hsiang Chang, Steve W.J. Kozlowski, H.S. Hung |
| رقم الانضمام: | edsbas.C70410 |
| قاعدة البيانات: | BASE |
| ردمك: | 9788513077702 8513077704 |
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| DOI: | 10.1109/TAFFC.2022.3176967 |