An Advanced Technique for the Detection of Pathological Gaits from Electromyography Signals: A Comprehensive Approach

Abstract

The aim of this study was to determine the most appropriate advanced methods for distinguishing the gait of healthy children (CO) from the gait of children with cerebral palsy (CP) based on electromyography (EMG) parameters and coactivations. An EMG database of 22 children (aged 4–11 years) was used in this study, which included 17 subjects in the CO group and 5 subjects in the CP group. EMG time parameters were calculated for the biceps femoris (BF) and semitendinosus (SE) muscles and coactivations for the rectus femoris (RF)/BF and RF/SE muscle pairs. To obtain a more accurate classification result, data augmentation was performed, and three classification algorithms were used: support vector machine (SVM), k-nearest neighbors (KNNs), and decision tree (DT). The accuracy of the root-mean-square (RMS) parameter and KNN algorithm was 95%, the precision was 94%, the sensitivity was 90%, the F1 score was 92%, and the area under the curve (AUC) score was 98%. The highest classification accuracy based on coactivations was achieved using the KNN algorithm (91–95%). It was determined that the KNN algorithm is the most effective, and muscle coactivation can be used as a reliable parameter in gait classification tasks.