Quantifying Pattern Recognition—Based Myoelectric Control of Multifunctional Transradial Prostheses
Open accessAbstract
We evaluated real-time myoelectric pattern recognition control of a virtual arm by transradial amputees. Five unilateral patients performed 10 wrist and hand movements using their amputated and intact arms. In order to demonstrate the value of information from intrinsic hand muscles, this data was included in EMG recordings from the intact arm. With both arms, motions were selected in approximately 0.2 s on average, and completed in less than 1.25 s. Approximately 99% of wrist movements were completed using either arm; however, the completion rate of hand movements was significantly lower for the amputated arm (53.9% +/- 14.2%) than for the intact arm ( 69.4% +/- 13.1%). For the amputated arm, average classification accuracy for only 6 movements-including a single hand grasp-was 93.1% +/- 4.1%, compared to 84.4% +/- 7.2% for all 10 movements. Use of 6 optimally-placed electrodes only reduced this accuracy to 91.5% +/- 4.9%. These results suggest that muscles in the residual forearm produce sufficient myoelectric information for real-time wrist control, but not for performing multiple hand grasps. The outcomes of this study could aid the development of a practical multifunctional myoelectric prosthesis for transradial amputees, and suggest that increased EMG information-such as made available through targeted muscle reinnervation-could improve control of these prostheses.
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That approach gave usable signal amplitude and allowed some prosthetic limb control in transradial amputees.
“This strategy has provided reasonable signal amplitude and some control of a prosthetic limb in patients undergoing transradial amputations (24–28).”
From A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees (2020) · cites this paper as [27]
The Introduction says powered transradial prostheses use forearm EMG signal amplitudes for limited control. The Abstract reports that five transradial amputees used residual-forearm signals to control a virtual arm, but the Discussion explicitly says they did not test a physical prosthesis. The paper does not establish a particular ‘reasonable’ signal amplitude. (LLM, conf=0.86)
Experimental — accuracy not yet measuredEarlier studies obtained prosthetic control signals by connecting to intact residual-limb muscles.
“In previous studies, investigators have provided prosthetic control signals by interfacing with intact muscles in the residual limb.”
From A regenerative peripheral nerve interface allows real-time control of an artificial hand in upper limb amputees (2020) · cites this paper as [27]
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- Authors
- Li, G., Schultz, A. E., & Kuiken, T. A.
- Affiliations
- Shirley Ryan AbilityLab (US)
- Northwestern University (US)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2010-04
- Volume
- 18
- Issue
- 2
- Pages
- 185-192
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- PMC
- PMC3024915
- OpenAlex
- W2096553993
- Cited by
- 443
- References
- 27
- Citations by year
- 2026: 5 · 2025: 16 · 2024: 17 · 2023: 30 · 2022: 25 · 2021: 23 · 2020: 27 · 2019: 27 · 2018: 34 · 2017: 38 · 2016: 31 · 2015: 26 · 2014: 43 · 2013: 40 · 2012: 22
- FWCI
- 19.07
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Northwestern University
- National Institutes of Health (5r01hd058000-05, 5r01hd043137-05)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01-HD-05-8000, R01 HD-043137)
- 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 control, transradial amputation, pattern recognition, EMG signal classification, virtual arm, wrist kinematics, hand grasp, electrode placement, targeted muscle reinnervation
- MeSH terms
- Adult, Aged, Algorithms, Amputation, Surgical, Arm, Artificial Limbs, Electrodes, Implanted, Forearm, Hand, Humans, Learning, Middle Aged, Motion, Pattern Recognition, Automated, Pilot Projects, Prosthesis Design, Radius, Wrist, Muscle, Skeletal, Hand Strength
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