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
Open accessAbstract
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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“In addition, detecting muscular activities and hand motions using electromyography (EMG) sensors has been widely applied to enable improved prostheses control for people with missing or damaged limbs [43,44,45,46].”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [45]
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Cites
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Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances
Zhang, S., Li, Y., Zhang, S., Shahabi, F., Xia, S., Deng, Y., & Alshurafa, N. (2022) · Sensors · doi:10.3390/s22041476 · Cited by 625
“In addition, detecting muscular activities and hand motions using electromyography (EMG) sensors has been widely applied to enable improved prostheses control for people with missing or damaged limbs [186, 209, 211, 240].”
MethodArtificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226826 · Cited by 291
Li, W., Shi, P. A., & Yu, H. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.621885 · Cited by 228
Kumar, P., Chauhan, S., & Awasthi, L. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09986-x · Cited by 228
Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks
Lee, K. H., Min, J. Y., & Byun, S. (2021) · Sensors · doi:10.3390/s22010225 · Cited by 133
Metadata
- 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
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-10-22
- Volume
- 19
- Issue
- 20
- Pages
- 4596
- ISSN
- 1424-8220 (ISSN-L)
- 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
- 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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