Personalized gait trajectory generation for lower-limb rehabilitation robots using anthropometric features.

Liangjie Tu, Shuai Zhao, Xinyi Tang, Rong Chen, Quanying Liu, Huihui Sun

Abstract read

In one paragraph

Article in iScience, 2026. 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

6 authors.

Liangjie TuCollege of Mechanical and Electrical Engineering, Huainan Normal University, Anhui 232038, Huainan, China.

Shuai ZhaoCollege of Mechanical Engineering, Zhejiang University of Technology, Zhejiang 310023, Hangzhou, China.

Xinyi TangCollege of Mechanical and Electrical Engineering, Huainan Normal University, Anhui 232038, Huainan, China.

Rong ChenCollege of Mechanical and Electrical Engineering, Huainan Normal University, Anhui 232038, Huainan, China.

Quanying LiuCollege of Mechanical and Electrical Engineering, Huainan Normal University, Anhui 232038, Huainan, China.

Huihui SunCollege of Mechanical and Electrical Engineering, Huainan Normal University, Anhui 232038, Huainan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Conventional gait generation schemes fail to accommodate variable walking speeds and diverse rehabilitation scenarios. This study constructs an improved Gaussian process regression (IGPR) model with hybrid kernel functions based on 12 anthropometric indicators, walking speed and training modes. The dataset consists of 42 healthy participants, with averaged hip joint angle trajectories

Indexed as

human-robot coordinated motion controlIGPRimproved gaussian process regression modellower-limb rehabilitation robotspersonalized gait trajectory

Identifiers

PMID42602969

DOI10.1016/j.isci.2026.116958

PMCPMC13475664

What OpenQuestion holds

Textmetadata

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.