Evidence map›Paper›PMID 37214786›Full record

ArticleFrontiers in neurorobotics2023

An advanced bionic knee joint mechanism with neural network controller.

Changxian Xu, Zhongbo Sun, Chen Wang, Xiujun Wu, Binglin Li, Liming Zhao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neurorobotics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 54% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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 1 institution.

Changxian XuDepartment of Control Engineering, Changchun University of Technology, Changchun, China.
Zhongbo SunDepartment of Control Engineering, Changchun University of Technology, Changchun, China.
Chen WangDepartment of Control Engineering, Changchun University of Technology, Changchun, China.
Xiujun WuDepartment of Control Engineering, Changchun University of Technology, Changchun, China.
Binglin LiDepartment of Control Engineering, Changchun University of Technology, Changchun, China.
Liming ZhaoDepartment of Control Engineering, Changchun University of Technology, Changchun, China.
Changchun University of Technology

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this article, a tensegrity-based knee mechanism is studied for developing a high-efficiency rehabilitation knee exoskeleton. Moreover, the kinematics and dynamics models of the knee mechanism are explored for bringing about further improvement in controller design. In addition, to estimate the performance of the bionic knee joint, based on the limit function of knee patella, the limit position functionality of the bionic knee joint is developed for enhancing the bionic property. Furthermore, to eliminate the noise item and other disturbances that are constantly generated in the rehabilitation process, a noise-tolerant zeroing neural network (NTZNN) algorithm is utilized to establish the controller. This indicates that the controller shows an anti-noise performance; hence, it is quite unique from other bionic knee mechanism controllers. Eventually, the anti-noise performance and the calculation of the precision of the NTZNN controller are verified through several simulation and contrast results.

Indexed as

bionic knee jointdynamicskinematicsnoise-tolerant zeroing neural network modeltensegrity

Identifiers

PMID37214786
PMCPMC10196168
OpenAlexW4377564651

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.