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.
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.