Gutenberg Open Science: Systematic comparison of the influence of different data preprocessing methods on the performance of gait classifications using machine learning

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-5261

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DC Field	Value	Language
dc.contributor.author	Burdack, Johannes	-
dc.contributor.author	Horst, Fabian	-
dc.contributor.author	Giesselbach, Sven	-
dc.contributor.author	Hassan, Ibrahim	-
dc.contributor.author	Daffner, Sabrina	-
dc.contributor.author	Schöllhorn, Wolfgang I.	-
dc.date.accessioned	2020-10-27T08:46:12Z	-
dc.date.available	2020-10-27T08:46:12Z	-
dc.date.issued	2020	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/5265	-
dc.description.abstract	Human movements are characterized by highly non-linear and multi-dimensional interactions within the motor system. Therefore, the future of human movement analysis requires procedures that enhance the classification of movement patterns into relevant groups and support practitioners in their decisions. In this regard, the use of data-driven techniques seems to be particularly suitable to generate classification models. Recently, an increasing emphasis on machine-learning applications has led to a significant contribution, e.g., in increasing the classification performance. In order to ensure the generalizability of the machine-learning models, different data preprocessing steps are usually carried out to process the measured raw data before the classifications. In the past, various methods have been used for each of these preprocessing steps. However, there are hardly any standard procedures or rather systematic comparisons of these different methods and their impact on the classification performance. Therefore, the aim of this analysis is to compare different combinations of commonly applied data preprocessing steps and test their effects on the classification performance of gait patterns. A publicly available dataset on intra-individual changes of gait patterns was used for this analysis. Forty-two healthy participants performed 6 sessions of 15 gait trials for 1 day. For each trial, two force plates recorded the three-dimensional ground reaction forces (GRFs). The data was preprocessed with the following steps: GRF filtering, time derivative, time normalization, data reduction, weight normalization and data scaling. Subsequently, combinations of all methods from each preprocessing step were analyzed by comparing their prediction performance in a six-session classification using Support Vector Machines, Random Forest Classifiers, Multi-Layer Perceptrons, and Convolutional Neural Networks. The results indicate that filtering GRF data and a supervised data reduction (e.g., using Principal Components Analysis) lead to increased prediction performance of the machine-learning classifiers. Interestingly, the weight normalization and the number of data points (above a certain minimum) in the time normalization does not have a substantial effect. In conclusion, the present results provide first domain-specific recommendations for commonly applied data preprocessing methods and might help to build more comparable and more robust classification models based on machine learning that are suitable for a practical application.	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin Mainz	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	796 Sport	de_DE
dc.subject.ddc	796 Athletic and outdoor sports and games	en_GB
dc.title	Systematic comparison of the influence of different data preprocessing methods on the performance of gait classifications using machine learning	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-5261	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 02 Sozialwiss., Medien u. Sport	de
jgu.organisation.number	7910	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Frontiers in Bioengineering and Biotechnology	de
jgu.journal.volume	8	de
jgu.pages.alternative	Art. 260	de
jgu.publisher.year	2020	-
jgu.publisher.name	Frontiers Media	de
jgu.publisher.place	Lausanne	de
jgu.publisher.uri	https://doi.org/10.3389/fbioe.2020.00260	de
jgu.publisher.issn	2296-4185	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	796	de
jgu.publisher.doi	10.3389/fbioe.2020.00260
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

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