  Human movements are characterized by highly non-linear and multi-dimensional
interactions within the motor system. Recently, an increasing emphasis on
machine-learning applications has led to a significant contribution to the
field of gait analysis, 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 3D 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. 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.
