Evidence map›Paper›PMID 42291219›Full record

ArticleiScience2026

Deep self-attention reinforcement learning adaptive gait planning and control for lower limb rehabilitation exoskeletons robot.

Pengfei Zhang, Xiangyang Li, Binrui Wang, Xueshan Gao, Tao Liu, Bin Zhang, Feifei Qin, Yiyi Zhang, Siwei Li

Abstract read
PubMed Full text at PMC Publisher
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

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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

9 authors.

Pengfei ZhangChina Jiliang University, Hangzhou, Zhejiang 310018, China.
Xiangyang LiChina Jiliang University, Hangzhou, Zhejiang 310018, China.
Binrui WangChina Jiliang University, Hangzhou, Zhejiang 310018, China.
Xueshan GaoGuangxi Key Laboratory of Special Engineering Equipment and Control, Guilin University of Aerospace Technology, Guangxi, China.
Tao LiuZhejiang University, Hangzhou, Zhejiang 310027, China.
Bin ZhangChina Jiliang University, Hangzhou, Zhejiang 310018, China.
Feifei QinChina Jiliang University, Hangzhou, Zhejiang 310018, China.
Yiyi ZhangChina Jiliang University, Hangzhou, Zhejiang 310018, China.
Siwei LiChina Jiliang University, Hangzhou, Zhejiang 310018, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aiming at the gait planning challenges of lower limb rehabilitation exoskeletons for adapting to user-specific gait characteristics, this study proposes an adaptive gait planning framework based on an improved deep deterministic policy gradient (DDPG) algorithm. We integrate self-attention mechanism into DDPG network, optimize attention parameters via cosine annealing SGD, and adopt Bayesian optimization to adjust hyperparameters adaptively. Simulation and experimental results show that the proposed algorithm outperforms traditional DDPG in gait trajectory generation, tracking accuracy and stability under variable gait parameters and unsteady human-robot interaction. This method improves the adaptive planning capability of exoskeletons, and provides a feasible scheme for personalized lower limb rehabilitation training.

Indexed as

BiomechanicsMachine learning

Identifiers

PMID42291219
PMCPMC13254900

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.