Literature DB >> 15475120

Applications of artificial neural nets in clinical biomechanics.

W I Schöllhorn1.   

Abstract

The purpose of this article is to provide an overview of current applications of artificial neural networks in the area of clinical biomechanics. The body of literature on artificial neural networks grew intractably vast during the last 15 years. Conventional statistical models may present certain limitations that can be overcome by neural networks. Artificial neural networks in general are introduced, some limitations, and some proven benefits are discussed.

Mesh:

Year:  2004        PMID: 15475120     DOI: 10.1016/j.clinbiomech.2004.04.005

Source DB:  PubMed          Journal:  Clin Biomech (Bristol, Avon)        ISSN: 0268-0033            Impact factor:   2.063


  16 in total

Review 1.  Neural network modelling and dynamical system theory: are they relevant to study the governing dynamics of association football players?

Authors:  Aviroop Dutt-Mazumder; Chris Button; Anthony Robins; Roger Bartlett
Journal:  Sports Med       Date:  2011-12-01       Impact factor: 11.136

2.  Determining the Online Measurable Input Variables in Human Joint Moment Intelligent Prediction Based on the Hill Muscle Model.

Authors:  Baoping Xiong; Nianyin Zeng; Yurong Li; Min Du; Meilan Huang; Wuxiang Shi; Guoju Mao; Yuan Yang
Journal:  Sensors (Basel)       Date:  2020-02-21       Impact factor: 3.576

3.  Low-back electromyography (EMG) data-driven load classification for dynamic lifting tasks.

Authors:  Deema Totah; Lauro Ojeda; Daniel D Johnson; Deanna Gates; Emily Mower Provost; Kira Barton
Journal:  PLoS One       Date:  2018-02-15       Impact factor: 3.240

4.  Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

Authors:  Fabian Horst; Alexander Eekhoff; Karl M Newell; Wolfgang I Schöllhorn
Journal:  PLoS One       Date:  2017-06-15       Impact factor: 3.240

5.  Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach.

Authors:  Andrea N Onodera; Isabel Cn Sacco; Wilson P Gavião Neto; Maria Isabel Roveri; Wagner R Oliveira
Journal:  PeerJ       Date:  2017-02-28       Impact factor: 2.984

6.  Explaining the unique nature of individual gait patterns with deep learning.

Authors:  Fabian Horst; Sebastian Lapuschkin; Wojciech Samek; Klaus-Robert Müller; Wolfgang I Schöllhorn
Journal:  Sci Rep       Date:  2019-02-20       Impact factor: 4.379

7.  Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning.

Authors:  Johannes Burdack; Fabian Horst; Sven Giesselbach; Ibrahim Hassan; Sabrina Daffner; Wolfgang I Schöllhorn
Journal:  Front Bioeng Biotechnol       Date:  2020-04-15

8.  Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network.

Authors:  Wuxiang Shi; Yurong Li; Dujian Xu; Chen Lin; Junlin Lan; Yuanbo Zhou; Qian Zhang; Baoping Xiong; Min Du
Journal:  Front Public Health       Date:  2021-04-16

Review 9.  Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.

Authors:  Reed D Gurchiek; Nick Cheney; Ryan S McGinnis
Journal:  Sensors (Basel)       Date:  2019-11-28       Impact factor: 3.576

10.  The role of AI technology in prediction, diagnosis and treatment of colorectal cancer.

Authors:  Chaoran Yu; Ernest Johann Helwig
Journal:  Artif Intell Rev       Date:  2021-07-04       Impact factor: 8.139

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