Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion. Issue 7 (19th May 2022)

• Record Type: Journal Article
• Authors: Dindorf, Carlo; Konradi, Jürgen; Wolf, Claudia; Taetz, Bertram; Bleser, Gabriele; Huthwelker, Janine; Werthmann, Friederike; Drees, Philipp; Fröhlich, Michael; Betz, Ulrich
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 25:Issue 7(2022)
• Page Start: 821
• Page End: 831
• Publication Date: 2022-05-19
• Subjects: Siamese neural networks -- triplet loss -- contrastive loss -- surface topography -- subject identification; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2021.1981884
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: 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.