ReviewExperimental brain research2019
Improving the functionality, robustness, and adaptability of myoelectric control for dexterous motion restoration.
Review in Experimental brain research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Predicting Motor Intent from Residual Neck Muscle Activity in Individuals with Neck Weakness from ALS.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
- HANDSON Hand: Strategies and Approaches for Competitive Success at CYBATHLON 2024.Bioengineering (Basel, Switzerland) · 2025Article
- Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles.Scientific data · 2022Article
- Continuous Semi-autonomous Prosthesis Control Using a Depth Sensor on the Hand.Frontiers in neurorobotics · 2022Article
- EMG feedback outperforms force feedback in the presence of prosthesis control disturbance.Frontiers in neuroscience · 2022Article
- Questioning Domain Adaptation in Myoelectric Hand Prostheses Control: An Inter- and Intra-Subject Study.Sensors (Basel, Switzerland) · 2021Article
- A compact system for simultaneous stimulation and recording for closed-loop myoelectric control.Journal of neuroengineering and rehabilitation · 2021Article
- Immersive augmented reality system for the training of pattern classification control with a myoelectric prosthesis.Journal of neuroengineering and rehabilitation · 2021Article
- Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future.Frontiers in neuroscience · 2021Review
- Biorealistic Control of Hand Prosthesis Augments Functional Performance of Individuals With Amputation.Frontiers in neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of advanced and effective human-machine interfaces, especially for amputees to control their prostheses, is very high priority and a very active area of research. An intuitive control method should retain an adequate level of functionality for dexterous operation, provide robustness against confounding factors, and supply adaptability for diverse long-term usage, all of which are current problems being tackled by researchers. This paper reviews the state-of-the-art, as well as, the limitations of current myoelectric signal control (MSC) methods. To address the research topic on functionality, we review different approaches to prosthetic hand control (DOF configuration, discrete or simultaneous, etc.), and how well the control is performed (accuracy, response, intuitiveness, etc.). To address the research on robustness, we review the confounding factors (limb positions, electrode shift, force variance, and inadvertent activity) that affect the stability of the control performance. Lastly, to address adaptability, we review the strategies that can automatically adjust the classifier for different individuals and for long-term usage. This review provides a thorough overview of the current MSC methods and helps highlight the current areas of research focus and resulting clinic usability for the MSC methods for upper-limb prostheses.
Indexed as
Identifiers
30506366What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.