A robust, real-time control scheme for multifunction myoelectric control
- PMID: 12848352
- DOI: 10.1109/TBME.2003.813539
A robust, real-time control scheme for multifunction myoelectric control
Abstract
This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.
Similar articles
-
Continuous myoelectric control for powered prostheses using hidden Markov models.IEEE Trans Biomed Eng. 2005 Jan;52(1):121-4. doi: 10.1109/TBME.2004.836492. IEEE Trans Biomed Eng. 2005. PMID: 15651571
-
A wavelet-based continuous classification scheme for multifunction myoelectric control.IEEE Trans Biomed Eng. 2001 Mar;48(3):302-11. doi: 10.1109/10.914793. IEEE Trans Biomed Eng. 2001. PMID: 11327498
-
Channel and feature selection in multifunction myoelectric control.Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:5182-5. doi: 10.1109/IEMBS.2007.4353509. Annu Int Conf IEEE Eng Med Biol Soc. 2007. PMID: 18003175
-
Myoelectric signal processing for control of powered limb prostheses.J Electromyogr Kinesiol. 2006 Dec;16(6):541-8. doi: 10.1016/j.jelekin.2006.08.006. Epub 2006 Oct 11. J Electromyogr Kinesiol. 2006. PMID: 17045489 Review.
-
Control of multifunctional prosthetic hands by processing the electromyographic signal.Crit Rev Biomed Eng. 2002;30(4-6):459-85. doi: 10.1615/critrevbiomedeng.v30.i456.80. Crit Rev Biomed Eng. 2002. PMID: 12739757 Review.
Cited by
-
Exploring augmented grasping capabilities in a multi-synergistic soft bionic hand.J Neuroeng Rehabil. 2020 Aug 25;17(1):116. doi: 10.1186/s12984-020-00741-y. J Neuroeng Rehabil. 2020. PMID: 32843058 Free PMC article.
-
Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future.Front Neurosci. 2021 Apr 26;15:621885. doi: 10.3389/fnins.2021.621885. eCollection 2021. Front Neurosci. 2021. PMID: 33981195 Free PMC article. Review.
-
Toward Hand Pattern Recognition in Assistive and Rehabilitation Robotics Using EMG and Kinematics.Front Neurorobot. 2021 May 13;15:659876. doi: 10.3389/fnbot.2021.659876. eCollection 2021. Front Neurorobot. 2021. PMID: 34054455 Free PMC article.
-
A special purpose embedded system for neural machine interface for artificial legs.Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:5207-10. doi: 10.1109/IEMBS.2011.6091288. Annu Int Conf IEEE Eng Med Biol Soc. 2011. PMID: 22255511 Free PMC article.
-
Noninvasive Human-Prosthesis Interfaces for Locomotion Intent Recognition: A Review.Cyborg Bionic Syst. 2021 Jun 4;2021:9863761. doi: 10.34133/2021/9863761. eCollection 2021. Cyborg Bionic Syst. 2021. PMID: 36285130 Free PMC article. Review.
Publication types
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Miscellaneous