Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Wolfgang Immanuel Schöllhorn"'
Publikováno v:
Frontiers in Bioengineering and Biotechnology, Vol 12 (2024)
IntroductionDespite the growing body of evidence highlighting the individuality in movement techniques, predominant models of motor learning, particularly during the acquisition phase, continue to emphasise generalised, person-independent approaches.
Externí odkaz:
https://doaj.org/article/102b4838af5f4adfb14c2f69017f3a5e
Autor:
Fabian Horst, Djordje Slijepcevic, Marvin Simak, Brian Horsak, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer
Publikováno v:
Computational and Structural Biotechnology Journal, Vol 21, Iss , Pp 3414-3423 (2023)
Human gait is a complex and unique biological process that can offer valuable insights into an individual’s health and well-being. In this work, we leverage a machine learning-based approach to model individual gait signatures and identify factors
Externí odkaz:
https://doaj.org/article/61c3a247c28e4e10995aa02ea5d8d83c
Autor:
Julius Baba Apidogo, Achraf Ammar, Atef Salem, Johannes Burdack, Wolfgang Immanuel Schöllhorn
Publikováno v:
Sports, Vol 12, Iss 1, p 5 (2023)
Effective sports training should be attuned to the athlete’s specific conditionings and characteristics. In motor learning research, two often neglected factors that influence this resonance are the learner’s athletic background and the structura
Externí odkaz:
https://doaj.org/article/93cfba7f744843bda65ceb8ce659c670
Autor:
Alexander Thomas John, Anna Barthel, Johanna Wind, Nikolas Rizzi, Wolfgang Immanuel Schöllhorn
Publikováno v:
Frontiers in Behavioral Neuroscience, Vol 16 (2022)
In search of more detailed explanations for body-mind interactions in physical activity, neural and physiological effects, especially regarding more strenuous sports activities, increasingly attract interest. Little is known about the underlying mani
Externí odkaz:
https://doaj.org/article/59c0089784e24e8a8e0b333c85726478
Autor:
Johannes Burdack, Fabian Horst, Daniel Aragonés, Alexander Eekhoff, Wolfgang Immanuel Schöllhorn
Publikováno v:
Frontiers in Psychology, Vol 11 (2020)
The scientific and practical fields—especially high-performance sports—increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by iden
Externí odkaz:
https://doaj.org/article/97609cf4438a4a53835e1fa261abd12c
Autor:
Manuel Bange, Gabriel Gonzalez-Escamilla, Nadine Sandra Claudia Lang, Hao Ding, Angela Radetz, Damian Marc Herz, Wolfgang Immanuel Schöllhorn, Muthuraman Muthuraman, Sergiu Groppa
Publikováno v:
Journal of Parkinson's disease. 12(5)
Background: Gait impairments are common in Parkinson’s disease (PD). The pathological mechanisms are complex and not thoroughly elucidated, thus quantitative and objective parameters that closely relate to gait characteristics are critically needed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc96f2c8ca1e22ba02c774eeecb72aaa
https://pubmed.ncbi.nlm.nih.gov/35570500
https://pubmed.ncbi.nlm.nih.gov/35570500
Autor:
Alexander Thomas John, Anna Barthel, Johanna Wind, Nikolas Rizzi, Wolfgang Immanuel Schöllhorn
Publikováno v:
Frontiers in behavioral neuroscience. 16
In search of more detailed explanations for body-mind interactions in physical activity, neural and physiological effects, especially regarding more strenuous sports activities, increasingly attract interest. Little is known about the underlying mani
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ffb678d16d84cb83dcd0322be39fc05a
https://pubmed.ncbi.nlm.nih.gov/35283739
https://pubmed.ncbi.nlm.nih.gov/35283739
Autor:
Djordje Slijepcevic, Fabian Horst, Sebastian Lapuschkin, Brian Horsak, Anna-Maria Raberger, Andreas Kranzl, Wojciech Samek, Christian Breiteneder, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer
Publikováno v:
ACM Transactions on Computing for Healthcare
Machine Learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification performance, most share a central limitation, their black-box character.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fa11c8bf5154fa8573d842fa38cb24b7