Multiple gait phase recognition using boosted classifiers based on sEMG signal and classification matrix

Recently, there have been many studies to bionic leg to help rehabilitation of lower limb amputees. These powered artificial prosthesis uses physical sensors and repeats trained gait movements in correction process. Therefore, existing methods have to use equal gait speed and movement regardless of person's intention. To solve this problem, in this paper, we propose detailed gait phase recognition method to classify three stance and one swing sub-phases by using heel and toe classifiers and classification matrix. EMG signals are extracted from four body locations of thigh such as Recus femoris, Vastus lateralis, Vastus medialis, Semitendinosus. And then we calculate feature values of the time-domain(MAV, VAR, WL, RMS, SSI) and apply two step classifiers. Experimental result shows that the accuracy of SVM heel classifier is 88%, that of SVM toe classifier is 94% when supervised extracted samples are used, and the accuracy of SVM heel classifier is 78.7%, that of SVM toe classifier is 79.3% when sequentially extracted samples are used, and the average accuracy of the proposed method(SVM) is 79% while that of existing method(SVM) is 61% in case of 4 sub-phase classification when sequentially extracted samples are used.