6533b851fe1ef96bd12a8dce
RESEARCH PRODUCT
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
Jürgen KonradiClaudia WolfMichael FröhlichBertram TaetzCarlo DindorfUlrich BetzPhilipp DreesGabriele BleserJanine Huthwelkersubject
MaleRelational databaseComputer science0206 medical engineeringFeature extractionPostureBiomedical EngineeringBioengineeringFeature selection02 engineering and technology03 medical and health sciencesAutomation0302 clinical medicineGait (human)Knowledge extractionmedicineHumansGaitComputingMethodologies_COMPUTERGRAPHICSSex Characteristicsbusiness.industryWork (physics)Reproducibility of ResultsPattern recognition030229 sport sciencesGeneral MedicineKnowledge Discovery020601 biomedical engineeringSagittal planeComputer Science ApplicationsBiomechanical PhenomenaHuman-Computer Interactionmedicine.anatomical_structureComputingMethodologies_PATTERNRECOGNITIONCoronal planeFemaleArtificial intelligencebusinessAlgorithmsdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-01-01 |