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Myoelectric control of prosthetic hands: state-of-the-art review

Overview of attention for article published in Medical Devices : Evidence and Research, July 2016
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Title
Myoelectric control of prosthetic hands: state-of-the-art review
Published in
Medical Devices : Evidence and Research, July 2016
DOI 10.2147/mder.s91102
Pubmed ID
Authors

Purushothaman Geethanjali

Abstract

Myoelectric signals (MES) have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on. The myoelectric control-based prosthetic hand aids to restore activities of daily living of amputees in order to improve the self-esteem of the user. All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand. Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features. The myoelectric control differs in operation to accommodate various external factors. This article reviews the state of the art of myoelectric prosthetic hand, giving description of each control strategy.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 592 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 <1%
United Kingdom 1 <1%
Peru 1 <1%
Unknown 588 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 104 18%
Student > Ph. D. Student 93 16%
Student > Bachelor 87 15%
Researcher 50 8%
Student > Doctoral Student 25 4%
Other 59 10%
Unknown 174 29%
Readers by discipline Count As %
Engineering 260 44%
Medicine and Dentistry 30 5%
Computer Science 24 4%
Neuroscience 23 4%
Nursing and Health Professions 10 2%
Other 45 8%
Unknown 200 34%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2016.
All research outputs
#20,824,878
of 25,584,565 outputs
Outputs from Medical Devices : Evidence and Research
#231
of 303 outputs
Outputs of similar age
#284,193
of 367,821 outputs
Outputs of similar age from Medical Devices : Evidence and Research
#12
of 16 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 303 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 12.7. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 367,821 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.