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Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals
Joint Authors
Malešević, Nebojša M.
Marković, Dimitrije
Kanitz, Gunter
Controzzi, Marco
Antfolk, Christian
Cipriani, Christian
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-02-18
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
We present a novel computational technique intended for the robust and adaptable control of a multifunctional prosthetic hand using multichannel surface electromyography.
The initial processing of the input data was oriented towards extracting relevant time domain features of the EMG signal.
Following the feature calculation, a piecewise modeling of the multidimensional EMG feature dynamics using vector autoregressive models was performed.
The next step included the implementation of hierarchical hidden semi-Markov models to capture transitions between piecewise segments of movements and between different movements.
Lastly, inversion of the model using an approximate Bayesian inference scheme served as the classifier.
The effectiveness of the novel algorithms was assessed versus methods commonly used for real-time classification of EMGs in a prosthesis control application.
The obtained results show that using hidden semi-Markov models as the top layer, instead of the hidden Markov models, ranks top in all the relevant metrics among the tested combinations.
The choice of the presented methodology for the control of prosthetic hand is also supported by the equal or lower computational complexity required, compared to other algorithms, which enables the implementation on low-power microcontrollers, and the ability to adapt to user preferences of executing individual movements during activities of daily living.
American Psychological Association (APA)
Malešević, Nebojša M.& Marković, Dimitrije& Kanitz, Gunter& Controzzi, Marco& Cipriani, Christian& Antfolk, Christian. 2018. Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1136916
Modern Language Association (MLA)
Malešević, Nebojša M.…[et al.]. Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1136916
American Medical Association (AMA)
Malešević, Nebojša M.& Marković, Dimitrije& Kanitz, Gunter& Controzzi, Marco& Cipriani, Christian& Antfolk, Christian. Vector Autoregressive Hierarchical Hidden Markov Models for Extracting Finger Movements Using Multichannel Surface EMG Signals. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1136916
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136916