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-Reference-Cited by-同舟云学术

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

Published:2020-11-02 Issue:3 Volume:24 Page:299-307
ISSN:1025-5842
Container-title:Computer Methods in Biomechanics and Biomedical Engineering
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering

Author:

Dindorf Carlo¹^ORCID,Konradi Jürgen²,Wolf Claudia²,Taetz Bertram³^ORCID,Bleser Gabriele⁴^ORCID,Huthwelker Janine⁵,Drees Philipp⁵,Fröhlich Michael¹^ORCID,Betz Ulrich²

Affiliation:

1. Department of Sports Science, Technische Universität Kaiserslautern, Kaiserslautern, Germany

2. Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany

3. Department Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Germany

4. Junior Research Group wear HEALTH, Technische Universität Kaiserslautern, Kaiserslautern, Germany

5. Department of Orthopedics and Trauma Surgery, University Medical Centre of the Johannes Gutenberg University Mainz, Mainz, Germany

Publisher

Informa UK Limited

Subject

Computer Science Applications,Human-Computer Interaction,Biomedical Engineering,General Medicine,Bioengineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10255842.2020.1828375

Reference52 articles.

1. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

2. Ensemble feature selection for high dimensional data: a new method and a comparative study

3. Sagittal Standing Posture, Back Pain, and Quality of Life Among Adults From the General Population

5. Risk factors for low back pain in women

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