Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion

Dindorf Carlo; Konradi Jurgen; Wolf ClaudiaTaetz BertramBleser GabrieleHuthwelker JanineWerthmann FriederikeDrees PhilippFroehlich MichaelBetz Ulrich

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Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion

机译：Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion

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Surface topography systems enable the capture of spinal dynamic movement; however, it is unclear whether vertebral dynamics are unique enough to identify individuals. Therefore, in this study, we investigated whether the identification of individuals is possible based on dynamic spinal data. Three different data representations were compared (automated extracted features using contrastive loss and triplet loss functions, as well as simple descriptive statistics). High accuracies indicated the possible existence of a personal spinal 'fingerprint', therefore enabling subject recognition. The present work forms the basis for an objective comparison of subjects and the transfer of the method to clinical use cases.

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《Computer methods in biomechanics and biomedical engineering》 |2022年第8期|821-831|共11页
作者
Dindorf Carlo; Konradi Jurgen; Wolf ClaudiaTaetz BertramBleser GabrieleHuthwelker JanineWerthmann FriederikeDrees PhilippFroehlich MichaelBetz Ulrich;
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作者单位

Johannes Gutenberg Univ Mainz;

Tech Univ Kaiserslautern;

German Res Ctr Artificial Intelligence;

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正文语种英语
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Siamese neural networks; triplet loss; contrastive loss; surface topography; subject identification;

Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion

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