Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning
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 decisio...
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Frontiers Media S.A.
2020-04-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fbioe.2020.00260/full |
| _version_ | 1818363345237966848 |
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| author | Johannes Burdack Fabian Horst Sven Giesselbach Sven Giesselbach Ibrahim Hassan Ibrahim Hassan Sabrina Daffner Wolfgang I. Schöllhorn Wolfgang I. Schöllhorn |
| author_facet | Johannes Burdack Fabian Horst Sven Giesselbach Sven Giesselbach Ibrahim Hassan Ibrahim Hassan Sabrina Daffner Wolfgang I. Schöllhorn Wolfgang I. Schöllhorn |
| author_sort | Johannes Burdack |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T21:47:00Z |
| format | Article |
| id | doaj.art-cc9b22356dcd4fdc95093e05fbbe06fd |
| institution | Directory Open Access Journal |
| issn | 2296-4185 |
| language | English |
| last_indexed | 2024-12-13T21:47:00Z |
| publishDate | 2020-04-01 |
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| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj.art-cc9b22356dcd4fdc95093e05fbbe06fd2022-12-21T23:30:23ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852020-04-01810.3389/fbioe.2020.00260507468Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine LearningJohannes Burdack0Fabian Horst1Sven Giesselbach2Sven Giesselbach3Ibrahim Hassan4Ibrahim Hassan5Sabrina Daffner6Wolfgang I. Schöllhorn7Wolfgang I. Schöllhorn8Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University, Mainz, GermanyDepartment of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University, Mainz, GermanyKnowledge Discovery, Fraunhofer-Institute of Intelligent Analysis and Information Systems (IAIS), Sankt Augustin, GermanyCompetence Center Machine Learning Rhine-Ruhr (ML2R), Dortmund, GermanyDepartment of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University, Mainz, GermanyFaculty of Physical Education, Zagazig University, Zagazig, EgyptQimoto, Doctors‘ Surgery for Sport Medicine and Orthopedics, Wiesbaden, GermanyDepartment of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University, Mainz, GermanyDepartment of Wushu, School of Martial Arts, Shanghai University of Sport, Shanghai, ChinaHuman 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.https://www.frontiersin.org/article/10.3389/fbioe.2020.00260/fullgait classificationdata selectiondata processingground reaction forcemulti-layer perceptronconvolutional neural network |
| spellingShingle | Johannes Burdack Fabian Horst Sven Giesselbach Sven Giesselbach Ibrahim Hassan Ibrahim Hassan Sabrina Daffner Wolfgang I. Schöllhorn Wolfgang I. Schöllhorn Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning Frontiers in Bioengineering and Biotechnology gait classification data selection data processing ground reaction force multi-layer perceptron convolutional neural network |
| title | Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning |
| title_full | Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning |
| title_fullStr | Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning |
| title_full_unstemmed | Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning |
| title_short | Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning |
| title_sort | systematic comparison of the influence of different data preprocessing methods on the performance of gait classifications using machine learning |
| topic | gait classification data selection data processing ground reaction force multi-layer perceptron convolutional neural network |
| url | https://www.frontiersin.org/article/10.3389/fbioe.2020.00260/full |
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