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

Published on Apr 1, 2014in IEEE Transactions on Biomedical Engineering4.49
· DOI :10.1109/TBME.2013.2296274
Sebastian Amsüss5
Estimated H-index: 5
(GAU: University of Göttingen),
Peter M. Goebel4
Estimated H-index: 4
+ 3 AuthorsDario Farina70
Estimated H-index: 70
(GAU: University of Göttingen)
Abstract
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.
  • References (36)
  • Citations (65)
References36
Newest
Jul 1, 2013 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Sebastian Amsüss (GAU: University of Göttingen)H-Index: 5
#2Liliana ParedesH-Index: 5
Last.Dario Farina (GAU: University of Göttingen)H-Index: 70
view all 6 authors...
#1Erik Scheme (UNB: University of New Brunswick)H-Index: 16
#2Bernard Hudgins (UNB: University of New Brunswick)H-Index: 17
Last.Kevin Englehart (UNB: University of New Brunswick)H-Index: 36
view all 3 authors...
#1Tatiana Tommasi (Idiap Research Institute)H-Index: 16
#2Francesco Orabona (Toyota)H-Index: 22
Last.Barbara Caputo (Idiap Research Institute)H-Index: 29
view all 4 authors...
#1Xinpu Chen (SJTU: Shanghai Jiao Tong University)H-Index: 4
#2Dingguo Zhang (SJTU: Shanghai Jiao Tong University)H-Index: 17
Last.Xiangyang Zhu (SJTU: Shanghai Jiao Tong University)H-Index: 21
view all 3 authors...
#1Anders Lyngvi Fougner (NTNU: Norwegian University of Science and Technology)H-Index: 7
#2Øyvind Stavdahl (NTNU: Norwegian University of Science and Technology)H-Index: 22
Last.Philip A. Parker (UNB: University of New Brunswick)H-Index: 22
view all 5 authors...
Aug 1, 2012 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
#1Caitlin L. Chicoine (Rehabilitation Institute of Chicago)H-Index: 2
#2Ann M. Simon (Rehabilitation Institute of Chicago)H-Index: 17
Last.Levi J. Hargrove (Rehabilitation Institute of Chicago)H-Index: 29
view all 3 authors...
Cited By65
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#1Ikram Brahim (University of Paris)
#2Islame Dhibou (University of Paris)
Last.Amine Nait-Ali (University of Paris)H-Index: 9
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#1Ana Matran-Fernandez (University of Essex)H-Index: 4
#2Itzel Jared Rodriguez Martinez (Sant'Anna School of Advanced Studies)
Last.Luca Citi (University of Essex)H-Index: 23
view all 0 authors...
#1Xiuhua Liu (PKU: Peking University)H-Index: 1
#2Zhihao Zhou (PKU: Peking University)H-Index: 5
Last.Qining Wang (PKU: Peking University)H-Index: 15
view all 4 authors...
#1Roberto Meattini (UNIBO: University of Bologna)H-Index: 2
#2Markus Nowak (DLR: German Aerospace Center)H-Index: 5
Last.Claudio Castellini (DLR: German Aerospace Center)H-Index: 2
view all 0 authors...
View next paperElectromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clinical use.