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

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....

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Bibliographic Details
Published in:IEEE transactions on biomedical engineering 2014-04, Vol.61 (4), p.1167-1176
Main Authors: Amsuss, Sebastian, Goebel, Peter M., Jiang, Ning, Graimann, Bernhard, Paredes, Liliana, Farina, Dario
Format: Article
Language:English
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
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Summary: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.
ISSN:0018-9294
1558-2531
DOI:10.1109/TBME.2013.2296274