Myoelectric Walking Mode Classification for Transtibial Amputees

• Published in: IEEE transactions on biomedical engineering Vol. 60; no. 10; pp. 2745 - 2750
• Main Authors: Miller, Jason D., Beazer, Mahyo Seyedali, Hahn, Michael E.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2013
• Subjects: Accuracy; Adult; Algorithms; Amputation Stumps - physiopathology; Electrodes; Electromyography; Electromyography - methods; Female; Gait; Humans; Legged locomotion; linear discriminant analysis (LDA); Male; Muscle Contraction; Muscle, Skeletal - physiopathology; Muscles; myoelectric control; Pattern Recognition, Automated - methods; Prosthetics; Reproducibility of Results; Sensitivity and Specificity; support vector machine (SVM); Support vector machines; Walking
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2013.2264466

Myoelectric control algorithms have the potential to detect an amputee's motion intent and allow the prosthetic to adapt to changes in walking mode. The development of a myoelectric walking mode classifier for transtibial amputees is outlined. Myoelectric signals from four muscles (tibialis anterior, medial gastrocnemius (MG), vastus lateralis, and biceps femoris) were recorded for five nonamputee subjects and five transtibial amputees over a variety of walking modes: level ground at three speeds, ramp ascent/descent, and stair ascent/descent. These signals were decomposed into relevant features (mean absolute value, variance, wavelength, number of slope sign changes, number of zero crossings) over three subwindows from the gait cycle and used to test the ability of classification algorithms for transtibial amputees using linear discriminant analysis (LDA) and support vector machine (SVM) classifiers. Detection of all seven walking modes had an accuracy of 97.9% for the amputee group and 94.7% for the nonamputee group. Misclassifications occurred most frequently between different walking speeds due to the similar nature of the gait pattern. Stair ascent/descent had the best classification accuracy with 99.8% for the amputee group and 100.0% for the nonamputee group. Stability of the developed classifier was explored using an electrode shift disturbance for each muscle. Shifting the electrode placement of the MG had the most pronounced effect on the classification accuracy for both samples. No increase in classification accuracy was observed when using SVM compared to LDA for the current dataset.