Journal: |
Frontiers in Bioengineering and Biotechnology
Frontiers
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Volume: |
8
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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
VectorMachines, RandomForest 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 domainspecific
recommendations for commonly applied data preprocessingmethods andmight
help to build more comparable and more robust classification models based on machine
learning that are suitable for a practical application.
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