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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 Engineering4.491
· DOI :10.1109/TBME.2013.2296274
Sebastian Amsüss5
Estimated H-index: 5
(GAU: University of Göttingen),
Peter M. Goebel5
Estimated H-index: 5
+ 3 AuthorsDario Farina80
Estimated H-index: 80
(GAU: University of Göttingen)
Sources
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 (88)
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References36
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The robustness of myo-electric prosthesis usage is largely influenced by user performance, where psychological factors (i.e. cognitive-skills, motor-skills and psychological status - such as motivation, will, and stress) play a prominent role. These factors become more important the more degrees of freedom (DOF) a multifunctional prosthesis provides. Despite the large amount of research efforts during the past decades on developing robust control and feedback methods, there has been limited atte...
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#1Lukai LiuH-Index: 5
#2Pu LiuH-Index: 6
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Time and frequency domain features of the surface electromyogram (EMG) signal acquired from multiple channels have frequently been investigated for use in controlling upper-limb prostheses. A common control method is EMG-based motion classification. We propose the use of EMG signal whitening as a preprocessing step in EMG-based motion classification. Whitening decorrelates the EMG signal and has been shown to be advantageous in other EMG applications including EMG amplitude estimation and EMG-fo...
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Jul 1, 2013 in EMBC (International Conference of the IEEE Engineering in Medicine and Biology Society)
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#2Liliana P. ParedesH-Index: 6
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Long-term functioning of a hand prosthesis is crucial for its acceptance by patients with upper limb deficit. In this study the reliability over days of the performance of pattern classification approaches based on surface electromyography (sEMG) signal for the control of upper limb prostheses was investigated. Recordings of sEMG from the forearm muscles were obtained across five consecutive days from five healthy subjects. It was demonstrated that the classification performance decreased monoto...
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This study describes a novel myoelectric control scheme that is capable of motion rejection. As an extension of the commonly used linear discriminant analysis (LDA), this system generates a confidence score for each decision, providing the ability to reject those with a score below a selected threshold. The thresholds are class-specific and affect only the rejection characteristics of the associated class. Furthermore, because the rejection stage is implemented using the outputs of the LDA, the ...
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Background The nonstationary property of electromyography (EMG) signals usually makes the pattern recognition (PR) based methods ineffective after some time in practical application for multinational prosthesis. The conventional EMG PR, which is accomplished in two separate steps: training and testing, ignores the mismatch between training and testing conditions and often discards the useful information in testing dataset.
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The performance of pattern recognition based myoelectric control has seen significant interest in the research community for many years. Due to a recent surge in the development of dexterous prosthetic devices, determining the clinical viability of multifunction myoelectric control has become paramount. Several factors contribute to differences between offline classification accuracy and clinical usability, but the overriding theme is that the variability of the elicited patterns increases great...
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At the time of this writing, the main means of control for polyarticulated self-powered hand prostheses is surface electromyography (sEMG). In the clinical setting, data collected from two electrodes are used to guide the hand movements selecting among a finite number of postures. Machine learning has been applied in the past to the sEMG signal (not in the clinical setting) with interesting results, which provide more insight on how these data could be used to improve prosthetic functionality. R...
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Pattern recognition can provide intuitive control of myoelectric prostheses. Currently, screen-guided training (SGT), in which individuals perform specific muscle contractions in sync with prompts displayed on a screen, is the common method of collecting the electromyography (EMG) data necessary to train a pattern recognition classifier. Prosthesis-guided training (PGT) is a new data collection method that requires no additional hardware and allows the individuals to keep their focus on the pros...
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