Cited by 332Muscle activation and electromyography studiesProsthetics and Rehabilitation RoboticsNeuroscience and Neural Engineering

The DOI matches its Crossref record; 106 of the 106 references it cites were found in a registry or an index.

Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

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Parajuli, Nawadita, Sreenivasan, Neethu, Bifulco, Paolo, Cesarelli, Mario, Savino, Sergio, Niola, Vincenzo, Esposito, Daniele, Hamilton, Tara J., Naik, Ganesh R., Gunawardana, Upul, Gargiulo, Gaetano D.·2019·Sensors

DOI 10.3390/s19204596PMC PMC6832440

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Abstract

Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations.

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Figure 1 General pattern recognition schemes [11,26,27]. EMG: electromyography.CC BY · PubMed Central

Figure 2 General pattern followed by the EMG-PR for real-time collected data from amputee.CC BY · PubMed Central

Figure 3 Real-time EMG-PR with embedded system.CC BY · PubMed Central

Figure 4 General representation of virtual prosthesis process.CC BY · PubMed Central

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Authors: Parajuli, N., Sreenivasan, N., Bifulco, P., Cesarelli, M., Savino, S., Niola, V., Esposito, D., Hamilton, T. J., Naik, G. R., Gunawardana, U., & Gargiulo, G. D.
Affiliations: Western Sydney University (AU)
University of Naples Federico II (IT)
Macquarie University (AU)
Corresponding author: Parajuli, Nawadita
ORCID: Sreenivasan, Neethu
Bifulco, Paolo
Cesarelli, Mario
Savino, Sergio
Niola, Vincenzo
Esposito, Daniele
Hamilton, Tara J.
Naik, Ganesh R.
Gunawardana, Upul
Gargiulo, Gaetano D.
Venue: Sensors
Publisher: MDPI AG
Published: 2019-10-22
Volume: 19
Issue: 20
Pages: 4596
ISSN: 1424-8220 (ISSN-L)
DOI: 10.3390/s19204596
PMC: PMC6832440
OpenAlex: W2981877040
Cited by: 332
References: 106
Citations by year: 2026: 30 · 2025: 54 · 2024: 52 · 2023: 64 · 2022: 69 · 2021: 39 · 2020: 22 · 2019: 2
FWCI: 2.65
Type: journal article
Language: en
Open access: gold
dx.doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Sensors (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
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: myoelectric prosthesis, upper limb amputation, EMG pattern recognition, real-time control, myoelectric control, upper limb prosthesis, amputee rehabilitation
MeSH terms: Algorithms, Artificial Limbs, Computer Systems, Electromyography, Hand, Humans, Pattern Recognition, Automated

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Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation

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