Classification of Human Learning Stages via Kernel Distribution Embeddings
Adaptive automation, automation which is responsive to the human's performance via the alteration of control laws or level of assistance, is an important tool for training humans to attain new skills when operating dynamical systems. When coupled with cognitive feedback, adaptive automati...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Control Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10378711/ |
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| author | Madeleine Shuhn-Tsuan Yuh Kendric Ray Ortiz Kylie Sue Sommer-Kohrt Meeko Oishi Neera Jain |
| author_facet | Madeleine Shuhn-Tsuan Yuh Kendric Ray Ortiz Kylie Sue Sommer-Kohrt Meeko Oishi Neera Jain |
| author_sort | Madeleine Shuhn-Tsuan Yuh |
| collection | DOAJ |
| description | Adaptive automation, automation which is responsive to the human's performance via the alteration of control laws or level of assistance, is an important tool for training humans to attain new skills when operating dynamical systems. When coupled with cognitive feedback, adaptive automation has the potential to further facilitate human training, but requires precise assessments of human progression through various learning stages. This is challenging because of the underlying dynamics, as well as the stochasticity inherent to human action. We propose a data-driven approach to assess learning stages in a complex quadrotor landing task that is responsive to stochastic, human-in-the-loop quadrotor dynamics. We represent each learning stage as a distribution of canonical trajectories for that learning stage, then employ kernel distribution embeddings in combination with a rule-based heuristic, to determine which canonical distribution a sample landing trajectory is closest to. We demonstrate our approach on experimental human subject data, and use our approach to evaluate the efficacy of cognitively-based adaptive automation designed to calibrate self-confidence. Our approach is more accurate than standard classification methods, such as nearest centroid assignment, which rely on metrics that are not inherently suited to analysis of trajectories of stochastic dynamical systems. |
| format | Article |
| id | doaj-art-c574e9e7ef3848e6b38194ddc1a3f430 |
| institution | Kabale University |
| issn | 2694-085X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Control Systems |
| spelling | doaj-art-c574e9e7ef3848e6b38194ddc1a3f4302025-01-09T00:02:59ZengIEEEIEEE Open Journal of Control Systems2694-085X2024-01-01310211710.1109/OJCSYS.2023.334870410378711Classification of Human Learning Stages via Kernel Distribution EmbeddingsMadeleine Shuhn-Tsuan Yuh0https://orcid.org/0000-0002-8767-9057Kendric Ray Ortiz1https://orcid.org/0000-0002-0468-7757Kylie Sue Sommer-Kohrt2https://orcid.org/0009-0003-6328-790XMeeko Oishi3https://orcid.org/0000-0003-3722-8837Neera Jain4https://orcid.org/0000-0001-6755-3484Purdue University, West Lafayette, IN, USAUniversity of New Mexico, Albuquerque, NM, USAPurdue University, West Lafayette, IN, USAUniversity of New Mexico, Albuquerque, NM, USAPurdue University, West Lafayette, IN, USAAdaptive automation, automation which is responsive to the human's performance via the alteration of control laws or level of assistance, is an important tool for training humans to attain new skills when operating dynamical systems. When coupled with cognitive feedback, adaptive automation has the potential to further facilitate human training, but requires precise assessments of human progression through various learning stages. This is challenging because of the underlying dynamics, as well as the stochasticity inherent to human action. We propose a data-driven approach to assess learning stages in a complex quadrotor landing task that is responsive to stochastic, human-in-the-loop quadrotor dynamics. We represent each learning stage as a distribution of canonical trajectories for that learning stage, then employ kernel distribution embeddings in combination with a rule-based heuristic, to determine which canonical distribution a sample landing trajectory is closest to. We demonstrate our approach on experimental human subject data, and use our approach to evaluate the efficacy of cognitively-based adaptive automation designed to calibrate self-confidence. Our approach is more accurate than standard classification methods, such as nearest centroid assignment, which rely on metrics that are not inherently suited to analysis of trajectories of stochastic dynamical systems.https://ieeexplore.ieee.org/document/10378711/Cyberphysical systemscognitive systems and controlhuman-in-the-loop systemsrule-based classificationkernel methods |
| spellingShingle | Madeleine Shuhn-Tsuan Yuh Kendric Ray Ortiz Kylie Sue Sommer-Kohrt Meeko Oishi Neera Jain Classification of Human Learning Stages via Kernel Distribution Embeddings IEEE Open Journal of Control Systems Cyberphysical systems cognitive systems and control human-in-the-loop systems rule-based classification kernel methods |
| title | Classification of Human Learning Stages via Kernel Distribution Embeddings |
| title_full | Classification of Human Learning Stages via Kernel Distribution Embeddings |
| title_fullStr | Classification of Human Learning Stages via Kernel Distribution Embeddings |
| title_full_unstemmed | Classification of Human Learning Stages via Kernel Distribution Embeddings |
| title_short | Classification of Human Learning Stages via Kernel Distribution Embeddings |
| title_sort | classification of human learning stages via kernel distribution embeddings |
| topic | Cyberphysical systems cognitive systems and control human-in-the-loop systems rule-based classification kernel methods |
| url | https://ieeexplore.ieee.org/document/10378711/ |
| work_keys_str_mv | AT madeleineshuhntsuanyuh classificationofhumanlearningstagesviakerneldistributionembeddings AT kendricrayortiz classificationofhumanlearningstagesviakerneldistributionembeddings AT kyliesuesommerkohrt classificationofhumanlearningstagesviakerneldistributionembeddings AT meekooishi classificationofhumanlearningstagesviakerneldistributionembeddings AT neerajain classificationofhumanlearningstagesviakerneldistributionembeddings |