The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal
Abstract
The human hand is a complex system, with a large number of degrees of freedom (DoFs), sensors embedded in its structure, actuators and tendons, and a complex hierarchical control. Despite this complexity, the efforts required to the user to carry out the different movements is quite small (albeit after an appropriate and lengthy training). On the contray, prosthetic hands are just a pale replication of the natural hand, with significantly reduced grasping capabilities and no sensory information delivered back to the user. Several attempts have been carried out to develop multifunctional prosthetic devices controlled by electromyographic (EMG) signals (myoelectric hands), harness (kinematic hands), dimensional changes in residual muscles, and so forth, but none ofthese methods permits the "natural" control of more than two DoFs. This article presents a review of the traditional methods used to control artificial hands by means of EMG signal, in both the clinical and research contexts, and introduces what could be the future developments in the control strategy of these devices.
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In the citing paper
“the formal scheme for the acquisition and analysis of EMG signals for the control of prosthetic devices is composed of several modules (Zecca et al., 2002) : (1) signal conditioning, pre-processing, and detection of onset movement (Micera et al., 1999) ; (2) feature extraction; (3) dimensionality reduction; (4) pattern recognition; and (5) off-line and on-line learning.”
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 Zecca et al., 2002
In the citing paper
“Among the different approaches for restoring the link between the external world and the nervous system, the use of implantable intrafascicular PNS interfaces is of great potential, as confirmed in previous experiments in humans [6,7].”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [7]
In the citing paper
“In fact, after appropriate control in experimental models to test their biocompatibility and efficacy [18], LIFEs have been recently used to control artificial devices [6,7,22-25] showing good results during short-term trials with human amputees.”
From Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces (2011) · cites this paper as [7]
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Samaee, S., & Kobravi, H. R. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101739 · Cited by 28
Biomimetic Prosthetic Hand Enabled by Liquid Crystal Elastomer Tendons
Lu, H., Zou, Z., Wu, X., Shi, C., Liu, Y., & Xiao, J. (2021) · Micromachines · doi:10.3390/mi12070736 · Cited by 29
Hierarchical Human-Inspired Control Strategies for Prosthetic Hands
Gentile, C., Cordella, F., & Zollo, L. (2022) · Sensors · doi:10.3390/s22072521 · Cited by 28
Metadata
- Authors
- Zecca, M., Micera, S., Carrozza, M. C., & Dario, P.
- Affiliations
- Scuola Superiore Sant'Anna (IT)
- Venue
- Critical Reviews? in Biomedical Engineering
- Publisher
- Begell House
- Published
- 2002
- Volume
- 30
- Issue
- 4-6
- Pages
- 459-485
- ISSN
- 0278-940X (ISSN-L)
- OpenAlex
- W4242470988
- Cited by
- 606
- References
- 0
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 16 · 2023: 23 · 2022: 19 · 2021: 18 · 2020: 26 · 2019: 37 · 2018: 37 · 2017: 41 · 2016: 31 · 2015: 31 · 2014: 57 · 2013: 44 · 2012: 38
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Critical Reviews in Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- 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
- prosthetic hand, myoelectric control, EMG signal processing, degrees of freedom, grasping, sensory feedback
- MeSH terms
- Algorithms, Artificial Limbs, Electromyography, Feedback, Hand, Hand Injuries, Humans, Pattern Recognition, Automated, Prosthesis Design, Quality Control, Signal Processing, Computer-Assisted, Neural Networks, Computer
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