Evidence map›Paper›PMID 36466163›Full record

ReviewFrontiers in neuroscience2022

Wearable super-resolution muscle-machine interfacing.

Huxi Wang, Siming Zuo, María Cerezo-Sánchez, Negin Ghahremani Arekhloo, Kianoush Nazarpour, Hadi Heidari

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.5field-weighted citation impact, top 19% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 20 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Huxi WangMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
Siming ZuoMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
María Cerezo-SánchezMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
Negin Ghahremani ArekhlooMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
Kianoush NazarpourNeuranics Ltd., Glasgow, United Kingdom.
Hadi HeidariMicroelectronics Lab, James Watt School of Engineering, The University of Glasgow, Glasgow, United Kingdom.
University of Glasgow · GBUniversity of Edinburgh · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Muscles are the actuators of all human actions, from daily work and life to communication and expression of emotions. Myography records the signals from muscle activities as an interface between machine hardware and human wetware, granting direct and natural control of our electronic peripherals. Regardless of the significant progression as of late, the conventional myographic sensors are still incapable of achieving the desired high-resolution and non-invasive recording. This paper presents a critical review of state-of-the-art wearable sensing technologies that measure deeper muscle activity with high spatial resolution, so-called super-resolution. This paper classifies these myographic sensors according to the different signal types (i.e., biomechanical, biochemical, and bioelectrical) they record during measuring muscle activity. By describing the characteristics and current developments with advantages and limitations of each myographic sensor, their capabilities are investigated as a super-resolution myography technique, including: (i) non-invasive and high-density designs of the sensing units and their vulnerability to interferences, (ii) limit-of-detection to register the activity of deep muscles. Finally, this paper concludes with new opportunities in this fast-growing super-resolution myography field and proposes promising future research directions. These advances will enable next-generation muscle-machine interfaces to meet the practical design needs in real-life for healthcare technologies, assistive/rehabilitation robotics, and human augmentation with extended reality.

Indexed as

electrical impedance tomographyelectromyographyforcemyographyhuman-computer interfacemagnetomyographymuscle-machine interfacesuper-resolutionwearable sensors

Identifiers

PMID36466163
PMCPMC9714306
OpenAlexW4309173195

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.