Automated Discrimination of Gait Patterns Based on sEMG Recognition

A general scheme of automated discrimination of gait patterns based on recognition of surface electromyogram of lower limbs is proposed to classify three different terrains and six different movement patterns. To verify the effectiveness of different feature extraction methods, time–frequency features such as RMS and MF, wavelet variance and matrix singularity value are employed to process the sEMG signals under different conditions. SVM is used to discriminate gait patterns based on the selected features. Comparison results indicate that feature extraction method based on matrix singularity value can obtain better results and over 92.5 % classification accuracy ratio can be achieved. Experimental result indicates the rationality and effectiveness of the proposed methods for feature extraction and pattern classification. The proposed scheme shows great potential in the application of lower limb assistance.