General method for automated feature extraction and selection and its application for gender classification and biomechanical knowledge discovery of sex differences in spinal posture during stance and gait. Issue 3 (17th February 2021)

• Record Type: Journal Article
• Authors: Dindorf, Carlo; Konradi, Jürgen; Wolf, Claudia; Taetz, Bertram; Bleser, Gabriele; Huthwelker, Janine; Drees, Philipp; Fröhlich, Michael; Betz, Ulrich
• Is Part Of: Computer methods in biomechanics and biomedical engineering. Volume 24:Issue 3(2021)
• Page Start: 299
• Page End: 307
• Publication Date: 2021-02-17
• Subjects: Ensemble feature selection -- classification -- surface topography -- spine -- motion -- gender; Biomechanics -- Data processing -- Periodicals; Biomedical engineering -- Periodicals; Biomechanics -- Periodicals; Biomedical Engineering -- methods -- Periodicals; Computing Methodologies -- Periodicals; 612.7
• DOI: 10.1080/10255842.2020.1828375
• Languages: English
• ISSNs: 1025-5842

Abstract

Abstract: Modern technologies enable to capture multiple biomechanical parameters often resulting in relational data. The current work proposes a generally applicable method comprising automated feature extraction, ensemble feature selection and classification to best capture the potentials of the data also for generating new biomechanical knowledge. Its benefits are demonstrated in the concrete biomechanically and medically relevant use case of gender classification based on spinal data for stance and gait. Very good results for accuracy were obtained using gait data. Dynamic movements of the lumbar spine in sagittal and frontal plane and of the pelvis in frontal plane best map gender differences.