Evidence map›Paper›PMID 38260744›Full record

ArticleFrontiers in bioengineering and biotechnology2023

Force/position-based velocity control strategy for the lower limb rehabilitation robot during active training: design and validation.

Junjie Tian, Hongbo Wang, Hao Lu, Yang Yang, Lianqing Li, Jianye Niu, Bo Cheng

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 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
–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

1 citing paper in PubMed.

  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

7 authors.

Junjie TianHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao, China.
Hongbo WangHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao, China.
Hao LuAcademy for Engineering and Technology, Fudan University, Shanghai, China.
Yang YangHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao, China.
Lianqing LiHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao, China.
Jianye NiuHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao, China.
Bo ChengQinhuangdao Hospital of Traditional Chinese Medicine, Qinhuangdao, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aiming at the shortcomings of most existing control strategies for lower limb rehabilitation robots that are difficult to guarantee trajectory tracking effect and active participation of the patient, this paper proposes a force/position-based velocity control (FPVC) strategy for the hybrid end-effector lower limb rehabilitation robot (HE-LRR) during active training. The configuration of HE-LRR is described and the inverse Jacobian analysis is carried out. Then, the FPVC strategy design is introduced in detail, including normal velocity planning and tangential velocity planning. The experimental platform for the HE-LRR system is presented. A series of experiments are conducted to validate the FPVC strategy's performance, including trajectory measurement experiments, force and velocity measurement experiments, and active participation experiments. Experimental studies show that the end effector possesses good following performance with the reference trajectory and the desired velocity, and the active participation of subjects can be adjusted by the control strategy parameters. The experiments have verified the rationality of the FPVC strategy, which can meet the requirements of trajectory tracking effect and active participation, indicating its good application prospects in the patient's robot-assisted active training.

Indexed as

active participationactive trainingrehabilitation robottrajectory trackingvelocity control

Identifiers

PMID38260744
PMCPMC10800786

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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.