Kinematic data analysis

Random forest:

Using the extracted kinematic parameters, build a random forest model to classify strides by animal groups (e.g., disease vs. healthy), based on parameters of individual step cycles, then save the results (prediction accuracy, parameter importance ranking, and confusion matrix).

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PCA

Using the extracted kinematic parameters, use principal component analysis to reduce dimensionality and cluster data by animal groups (e.g., disease vs. healthy), based on parameters of individual step cycles, then save the results (PCA plot, including the explained variance of two principal components).