Evidence map›Paper›PMID 32887326›Full record

ArticleSensors (Basel, Switzerland)2020

Continuous Estimation of Knee Joint Angle Based on Surface Electromyography Using a Long Short-Term Memory Neural Network and Time-Advanced Feature.

Xunju Ma, Yali Liu, Qiuzhi Song, Can Wang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
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

13 citing papers in PubMed.

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  8. [Research on gait recognition and prediction based on optimized machine learning algorithm].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022
    Article
  9. Review
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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

4 authors.

Xunju MaSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Yali LiuSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.ORCID 0000-0001-5174-2598
Qiuzhi SongSchool of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
Can WangShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.ORCID 0000-0002-0914-3994

Funding

China Postdoctoral Science Foundation-funded project 2019M660478Ministry of Science and Technology national key R&D program 2017YFB1300500National Natural Science Foundation of China 51905035
6 · The paper itself

Abstract

Continuous joint angle estimation based on a surface electromyography (sEMG) signal can be used to improve the man-machine coordination performance of the exoskeleton. In this study, we proposed a time-advanced feature and utilized long short-term memory (LSTM) with a root mean square (RMS) feature and its time-advanced feature (RMSTAF; collectively referred to as RRTAF) of sEMG to estimate the knee joint angle. To evaluate the effect of joint angle estimation, we used root mean square error (RMSE) and cross-correlation coefficient

Indexed as

Knee JointNeural Networks, ComputerElectromyographyHumansMuscle, SkeletalestimationfeatureLSTMsEMGtime-advance

Identifiers

PMID32887326
PMCPMC7506963

What OpenQuestion holds

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Registered trials

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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.