Evidence map›Paper›PMID 35607430›Full record

ArticleApplied bionics and biomechanics2022

Real-Time Control of Intelligent Prosthetic Hand Based on the Improved TCN.

Xiaoguang Liu, Jiawei Wang, Tingwen Han, Cunguang Lou, Tie Liang, Hongrui Wang, Xiuling Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied bionics and biomechanics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 43% 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, 1 synthesis or guideline pooled it, 6 citations in OpenAlex.

  1. Pooled it
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

7 authors at 1 institution in 1 country.

Xiaoguang LiuCollege of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.
Jiawei WangCollege of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.ORCID https://orcid.org/0000-0002-5398-0810
Tingwen HanCollege of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.
Cunguang LouCollege of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.
Tie LiangCollege of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.
Hongrui WangCollege of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.
Xiuling LiuCollege of Electronic and Information Engineering, Hebei University, Baoding, Hebei, China.ORCID https://orcid.org/0000-0002-1871-1017
Hebei University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intelligent prosthetic hand is an important branch of intelligent robotics. It can remotely replace humans to complete various complex tasks and also help humans to complete rehabilitation training. In human-computer interaction technology, the prosthetic hand can be accurately controlled by surface electromyography (sEMG). This paper proposes a new multichannel fusion scheme (MSFS) to extend the virtual channels of sEMG and improve the accuracy of gesture recognition. In addition, the Temporal Convolutional Network (TCN) in deep learning has been improved to enhance the performance of the network. Finally, the sEMG is collected by the Myo armband and the prosthetic hand is controlled in real time to validate the new method. The experimental results show that the method proposed in this paper can improve the accuracy of the control intelligent prosthetic hand, and the accuracy rate is 93.69%.

Identifiers

PMID35607430
PMCPMC9124145
OpenAlexW4280600921

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.