Fig 1.
Proposed approach for detecting abnormalities in knees from sEMG signals recorded during walking.
Fig 2.
Samples used to train and evaluate classifiers.
(A) With original data. (B) With SMOTE oversampled data.
Table 1.
Confusion matrix.
Table 2.
Parameters of machine learning models.
Table 3.
Classifier in terms of different performance metrics with different pre-processing techniques with SMOTE.
Fig 3.
Confusion matrix with Extra Trees classifier.
(A-C) With original data. (D-F) With SMOTE oversampled data.
Fig 4.
ROC curve.
Table 4.
Assessing the effectiveness of the proposed methodology by comparing its performance with existing literature studies utilizing similar datasets (in %).