Data Acquisition Through Participatory Design for Automated Rehabilitation Assessment
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913633553874944 |
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| author | Ahmed, Tamim Guo, Zhaoyi Khan, Mohammod Shaikh Sadid Rikakis, Thanassis Kelliher, Aisling |
| author_facet | Ahmed, Tamim Guo, Zhaoyi Khan, Mohammod Shaikh Sadid Rikakis, Thanassis Kelliher, Aisling |
| contents | Through participatory design, we are developing a computational system for the semi-automated assessment of the Action Research Arm Test (ARAT) for stroke rehabilitation. During rehabilitation assessment, clinicians rate movement segments and components in the context of overall task performance. Clinicians change viewing angles to assess particular components. Through studies with clinicians, we develop a system that includes: a) unobtrusive multi-camera capture, b) a segmentation interface for non-expert segmentors, and c) a rating interface for expert clinicians. Five clinicians independently captured 1800 stroke survivor videos with <5$\%$ errors. Three segmentors have segmented 760 of these videos, averaging 20 seconds per segment. They favor the recommended camera view $>$ 90\%. Multiple clinicians have rated the segmented videos while reporting minimal problems. The complete data will be used for training an automated segmentation and rating system that empowers the clinicians as the ratings will be compatible with clinical practice and intuition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01374 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Acquisition Through Participatory Design for Automated Rehabilitation Assessment Ahmed, Tamim Guo, Zhaoyi Khan, Mohammod Shaikh Sadid Rikakis, Thanassis Kelliher, Aisling Human-Computer Interaction Through participatory design, we are developing a computational system for the semi-automated assessment of the Action Research Arm Test (ARAT) for stroke rehabilitation. During rehabilitation assessment, clinicians rate movement segments and components in the context of overall task performance. Clinicians change viewing angles to assess particular components. Through studies with clinicians, we develop a system that includes: a) unobtrusive multi-camera capture, b) a segmentation interface for non-expert segmentors, and c) a rating interface for expert clinicians. Five clinicians independently captured 1800 stroke survivor videos with <5$\%$ errors. Three segmentors have segmented 760 of these videos, averaging 20 seconds per segment. They favor the recommended camera view $>$ 90\%. Multiple clinicians have rated the segmented videos while reporting minimal problems. The complete data will be used for training an automated segmentation and rating system that empowers the clinicians as the ratings will be compatible with clinical practice and intuition. |
| title | Data Acquisition Through Participatory Design for Automated Rehabilitation Assessment |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.01374 |