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Cited by 495Muscle activation and electromyography studiesProsthetics and Rehabilitation RoboticsNeuroscience and Neural Engineering

Control of upper limb prostheses: terminology and proportional myoelectric control-a review

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Fougner, Anders, Stavdahl, Øyvind, Kyberd, Peter J., Losier, Yves G., Parker, Philip A.·2012·IEEE Transactions on Neural Systems and Rehabilitation Engineering
DOI 10.1109/tnsre.2012.2196711
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Abstract

The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.

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  • Sophisticated approaches have been developed to control prosthetic hands intuitively.

    “Major strides have been made in the development of approaches to control these hands either by decoding motor intent from myoelectric signals (Fougner et al., 2012;Schultz and Kuiken, 2011) or from the activation of neurons in motor cortex (Bensmaia and Miller, 2014;Wodlinger et al., 2015).”

    From Biomimetic approaches to bionic touch through a peripheral nerve interface (2015) · cites this paper as Fougner et al., 2012

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Authors
Fougner, A., Stavdahl, Ø., Kyberd, P. J., Losier, Y. G., & Parker, P. A.
Affiliations
  • Norwegian University of Science and Technology (NO)
  • University of New Brunswick (CA)
ORCID
Venue
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2012-09
Volume
20
Issue
5
Pages
663-677
ISSN
  • 1534-4320 (ISSN-L)
  • 1558-0210
DOI
10.1109/tnsre.2012.2196711
OpenAlex
W1971702085
Cited by
495
References
104
Citations by year
2026: 15 · 2025: 17 · 2024: 32 · 2023: 41 · 2022: 37 · 2021: 48 · 2020: 43 · 2019: 53 · 2018: 46 · 2017: 48 · 2016: 32 · 2015: 28 · 2014: 39 · 2013: 16
FWCI
2.31
Type
journal article
Language
en
Open access
Licence
other-oa
Locations
  • IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
  • PubMed (publishedVersion)
  • BIBSYS Brage (BIBSYS (Norway)) (submittedVersion)
  • NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
Topics
  • Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
  • Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neural Engineering
Keywords
upper limb prosthesis, myoelectric control, outcome measures
MeSH terms
Artificial Limbs, Biofeedback, Psychology, Computer Simulation, Electromyography, Humans, Models, Biological, Movement Disorders, Terminology as Topic, Therapy, Computer-Assisted, Muscle, Skeletal, Upper Extremity

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