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

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.

Keywords:

• Ensemble feature selection
• classification
• surface topography
• spine
• motion
• gender

Acknowledgements

Most of all we would like to thank the participants of this study. The DIERS Company is acknowledged for donating the measuring device the University Medical Center of the Johannes Gutenberg University Mainz for the purpose of research. Finally, we express our gratitude to Kjell Heitmann and Amira Basic for technological support and to Helmut Diers for manifold contributions in this project.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the BMBF under grant numbers 16SV7115 and 03IHS075B.