Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917870227685376 |
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| author | Sziburis, Tim Blex, Susanne Glasmachers, Tobias Iossifidis, Ioannis |
| author_facet | Sziburis, Tim Blex, Susanne Glasmachers, Tobias Iossifidis, Ioannis |
| contents | The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on levels and stages of movement disorders. This work presents a preliminary study for differentiating individual motion patterns using a dataset of 3D upper-limb transport trajectories measured in task-space. Identifying individuals by deep time series learning can be a key step to abstracting individual motion properties. In this study, a classification accuracy of about 95% is reached for a subset of nine, and about 78% for the full set of 31 individuals. This provides insights into the separability of patient attributes by exerting a simple standardized task to be transferred to portable systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2412_12016 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation Sziburis, Tim Blex, Susanne Glasmachers, Tobias Iossifidis, Ioannis Neural and Evolutionary Computing Machine Learning Quantitative Methods The identification of individual movement characteristics sets the foundation for the assessment of personal rehabilitation progress and can provide diagnostic information on levels and stages of movement disorders. This work presents a preliminary study for differentiating individual motion patterns using a dataset of 3D upper-limb transport trajectories measured in task-space. Identifying individuals by deep time series learning can be a key step to abstracting individual motion properties. In this study, a classification accuracy of about 95% is reached for a subset of nine, and about 78% for the full set of 31 individuals. This provides insights into the separability of patient attributes by exerting a simple standardized task to be transferred to portable systems. |
| title | Deep-learning-based identification of individual motion characteristics from upper-limb trajectories towards disorder stage evaluation |
| topic | Neural and Evolutionary Computing Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2412.12016 |