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.
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Recent research tried to implement a continuous classifier able to quickly switch from one hand task state to another.
“recent research tried to implement a continuous classifier able to quickly switch from one state (i.e., hand task) to another (Englehart and Hudgins, 2003; Carrozza et al., 2004)”
From A critical review of interfaces with the peripheral nervous system for the control of neuroprostheses and hybrid bionic systems (2005) · cites this paper as Englehart and Hudgins, 2003
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- Authors
- Englehart, K., & Hudgins, B.
- Affiliations
- University of New Brunswick (CA)
- ORCID
- Englehart, K.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2003-07
- Volume
- 50
- Issue
- 7
- Pages
- 848-854
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2165619603
- Cited by
- 1,743
- References
- 13
- Citations by year
- 2026: 63 · 2025: 75 · 2024: 81 · 2023: 99 · 2022: 114 · 2021: 115 · 2020: 121 · 2019: 118 · 2018: 129 · 2017: 106 · 2016: 91 · 2015: 91 · 2014: 98 · 2013: 87 · 2012: 68
- FWCI
- 12.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- myoelectric control, upper limb prosthesis, pattern recognition, real-time classification, muscle activation patterns, classifier training, embedded control systems, prosthetic control
- MeSH terms
- Action Potentials, Algorithms, Arm, Artificial Limbs, Electric Stimulation, Electromyography, Feedback, Forearm, Humans, Pattern Recognition, Automated, Prosthesis Design, Signal Processing, Computer-Assisted, Therapy, Computer-Assisted, Wrist Joint, Muscle, Skeletal
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