AnomGait: Data-Driven Extraction of Movement Features Using Contrastive Learning
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Author / Producer
Date
2024
Publication Type
Conference Paper
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yes
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Abstract
To describe movement patterns in gait, interpretable handcrafted features such as step length and step width are typically used. Current movement analysis tools allow the generation of rich, high-dimensional data for each individual, which is then, however, again reduced to traditional, single-dimension parameters. We here apply contrastive learning to high-dimensional gait data sets from neurological patients and healthy individuals and attempt to distill data-driven features to improve separation.
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Publication status
published
External links
Book title
Converging Clinical and Engineering Research on Neurorehabilitation V
Journal / series
Volume
32
Pages / Article No.
515 - 518
Publisher
Springer
Event
6th International Conference on Neurorehabilitation (ICNR 2024)
Edition / version
Methods
Software
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Subject
Organisational unit
09670 - Vogt, Julia / Vogt, Julia
03994 - Taylor, William R. / Taylor, William R.