Evidence map›Paper›PMID 41439883›Full record

ArticleBiomimetics (Basel, Switzerland)2025

Robot-Assisted Dynamic Interaction of Hemiplegic Upper Limbs with Complex Objects Based on Enhanced Feedforward-Impedance Control.

Jing Bai, Ruoyi Zhu, Yicheng Jiang, Xiaofei Du

Abstract read
In one paragraph

Article in Biomimetics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jing BaiSchool of Mechanical Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
Ruoyi ZhuSchool of Mechanical Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
Yicheng JiangSchool of Mechanical Engineering, Nanjing Institute of Technology, Nanjing 211167, China.
Xiaofei DuSchool of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.

Funding

Natural Science Foundation of Jiangsu Province BK20210930
6 · The paper itself

Abstract

Current upper-limb rehabilitation robots primarily focus on training tasks involving free movements or static interactions with rigid objects. These paradigms lack simulation of complex object manipulation tasks encountered in daily life, thereby limiting the training of patients' high-level sensorimotor integration capabilities. To address this gap, this study proposes an innovative robotic rehabilitation training system designed for functional occupational therapy. Specifically, the task of transporting a water cup was abstracted into a cup-ball system integrated with a robotic arm. The ball was modeled as a point mass, and kinematic and dynamic analyses of the system were conducted. A visual tracking method was employed to monitor the ball's motion and calculate its slosh angle. Owing to the impaired fine motor control in stroke patients, a sloshing suppression control strategy integrating exponential filtering, feedforward force compensation, and impedance control was proposed to prevent the ball from spilling. Experiments validated the effectiveness of the proposed method. The results indicated that with full compensation, the oscillation rate of the ball was significantly reduced, and the smoothness of the hand force was markedly improved. This study presents an effective method for addressing dynamic uncertainty in rehabilitation robot training, thus significantly improving the functional relevance of the training.

Indexed as

dynamic interactionfeedforward controlrehabilitation trainingrobotsloshing suppression

Identifiers

PMID41439883
PMCPMC12730735

What OpenQuestion holds

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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.