A Decision Tree Model for Analysis and Judgment of Lower Limb Movement Comfort Level

To address the problem of ambiguity and one-sidedness in the evaluation of comprehensive comfort perceptions during lower limb exercise, this paper deconstructs the comfort perception into two dimensions: psychological comfort and physiological comfort. Firstly, we designed a fixed-length weightless lower limb squat exercise test to collect original psychological comfort data and physiological comfort data. The principal component analysis and physiological comfort index algorithm were used to extract the comfort index from the original data. Secondly, comfort degrees for each sample were obtained by performing K-means++ to cluster normalized comfort index. Finally, we established a decision tree model for lower limb comfort level analysis and determination. The results showed that the classification accuracy of the model reached 95.8%, among which the classification accuracy of the four comfort levels reached 95.2%, 97.3%, 92.9%, and 97.8%, respectively. In order to verify the advantages of this paper, the classification results of this paper were compared with the classification results of four supervised classification algorithms: Gaussian Parsimonious Bayes, linear SVM, cosine KNN and traditional CLS decision tree.