Using Causal Trees to Estimate Personalized Task Difficulty in Post-Stroke Individuals
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911790513782784 |
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| author | Dennler, Nathaniel Nikolaidis, Stefanos Matarić, Maja |
| author_facet | Dennler, Nathaniel Nikolaidis, Stefanos Matarić, Maja |
| contents | Adaptive training programs are crucial for recovery post stroke. However, developing programs that automatically adapt depends on quantifying how difficult a task is for a specific individual at a particular stage of their recovery. In this work, we propose a method that automatically generates regions of different task difficulty levels based on an individual's performance. We show that this technique explains the variance in user performance for a reaching task better than previous approaches to estimating task difficulty. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_04109 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Causal Trees to Estimate Personalized Task Difficulty in Post-Stroke Individuals Dennler, Nathaniel Nikolaidis, Stefanos Matarić, Maja Robotics Human-Computer Interaction Machine Learning Adaptive training programs are crucial for recovery post stroke. However, developing programs that automatically adapt depends on quantifying how difficult a task is for a specific individual at a particular stage of their recovery. In this work, we propose a method that automatically generates regions of different task difficulty levels based on an individual's performance. We show that this technique explains the variance in user performance for a reaching task better than previous approaches to estimating task difficulty. |
| title | Using Causal Trees to Estimate Personalized Task Difficulty in Post-Stroke Individuals |
| topic | Robotics Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2403.04109 |