Self-Correcting Pattern Recognition System of Surface EMG Signals for Upper Limb Prosthesis Control

• Published in: IEEE transactions on biomedical engineering Vol. 61; no. 4; pp. 1167 - 1176
• Main Authors: Amsuss, Sebastian, Goebel, Peter M., Jiang, Ning, Graimann, Bernhard, Paredes, Liliana, Farina, Dario
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Arm - physiology; Artificial Limbs; Artificial neural networks; Artificial neural networks (ANNs); Classification; Classification algorithms; Electrodes; Electromyography - methods; Female; Humans; Male; Materials; Middle Aged; myoelectric control; Neural networks; Neural Networks (Computer); Pattern recognition; pattern recognition (PR); Pattern Recognition, Automated - methods; Prostheses; Prosthetics; robustness; Signal Processing, Computer-Assisted; Training; upper limb prostheses; Young Adult
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2013.2296274

Pattern recognition methods for classifying user motion intent based on surface electromyography developed by research groups in well-controlled laboratory conditions are not yet clinically viable for upper limb prosthesis control, due to their limited robustness in users' real-life situations. To address this problem, a novel postprocessing algorithm, aiming to detect and remove misclassifications of a pattern recognition system of forearm and hand motions, is proposed. Using the maximum likelihood calculated by a classifier and the mean global muscle activity of the forearm, an artificial neural network was trained to detect potentially erroneous classification decisions. This system was compared to four previously proposed classification postprocessing methods, in both able-bodied and amputee subjects. Various nonstationarities were included in the experimental protocol to account for challenges posed in real-life settings, such as different contraction levels, static and dynamic motion phases, and effects induced by day-to-day transfers, such as electrode shifts, impedance changes, and psychometric user variability. The improvement in classification accuracy with respect to the unprocessed classifier ranged from 4.8% to 31.6%, depending on the scenarios investigated. The system significantly reduced misclassifications to wrong active classes and is thus a promising approach for improving the robustness of hand prosthesis controllability.