Paula Schumann Dipl.Person
Person
- Chair of Biomedical Engineering- Academic Staff
Affiliations
Identification Numbers
| Scopus author ID | 57217207915 |
|---|
Related content
- Research output
Using machine learning algorithms to detect fear of falling in people with multiple sclerosis in standardized gait analysis
Schumann, P., Trentzsch, K., Stölzer-Hutsch, H., Jochim, T., Scholz, M., Malberg, H. & Ziemssen, T., Aug 2024, In: Multiple sclerosis and related disorders. 88 (2024), 7 p., 105721.Research output: Contribution to journal › Research article › Contributed › peer-review
Use of machine learning algorithms to detect fear of falling in people with multiple sclerosis
Trentzsch, K., Schumann, P., Stölzer-Hutsch, H., Stecher, N., Malberg, H. & Ziemssen, T., Mar 2024, In: Clinical Neurophysiology. 159, p. e32-e33Research output: Contribution to journal › Conference article › Contributed › peer-review
Torsobarography: Intra-Observer Reliability Study of a Novel Posture Analysis Based on Pressure Distribution
Stecher, N., Heinke, A., Żurawski, A. Ł., Harder, M. R., Schumann, P., Jochim, T. & Malberg, H., 24 Jan 2024, In: Sensors. 24, 3, 768.Research output: Contribution to journal › Research article › Contributed › peer-review
Detection of Fall Risk in Multiple Sclerosis by Gait Analysis—An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms
Schumann, P., Scholz, M., Trentzsch, K., Jochim, T., Śliwiński, G., Malberg, H. & Ziemssen, T., Nov 2022, In: Brain sciences. 12, 11, 1477.Research output: Contribution to journal › Research article › Contributed › peer-review
Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis
Trentzsch, K., Schumann, P., Śliwiński, G., Bartscht, P., Haase, R., Schriefer, D. & Zink, A. & 4 others, Heinke, A., Jochim, T., Malberg, H. & Ziemssen, T., 7 Aug 2021, In: Brain sciences. 11, 8, 1049.Research output: Contribution to journal › Research article › Contributed › peer-review