Evidence map›Paper›PMID 39686042›Full record

ArticleSensors (Basel, Switzerland)2024

A Linear Rehabilitative Motion Planning Method with a Multi-Posture Lower-Limb Rehabilitation Robot.

Xincheng Wang, Musong Lin, Lingfeng Sang, Hongbo Wang, Yongfei Feng, Jianye Niu, Hongfei Yu, Bo Cheng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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

8 authors.

Xincheng WangHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao 066000, China.ORCID 0000-0002-8752-3669
Musong LinDepartment of Environmental Engineering, Hebei University of Environmental Engineering, Qinhuangdao 066000, China.
Lingfeng SangNingbo Key Laboratory of Aging Health Equipment and Service Technology, Ningbo Polytechnic, Ningbo 315211, China.ORCID 0000-0002-1029-8408
Hongbo WangHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao 066000, China.
Yongfei FengFaculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China.
Jianye NiuHebei Provincial Key Laboratory of Parallel Robot and Mechatronic System, Yanshan University, Qinhuangdao 066000, China.ORCID 0000-0001-8040-8396
Hongfei YuCollaborative Innovation Center for Port Industry Development in Coastal Areas, Qinhuangdao 066000, China.
Bo ChengQinhuangdao Hospital of Traditional Chinese Medicine, Qinhuangdao 066000, China.

Funding

Hebei Province Higher Education Humanities and Social Sciences Research Project (Youth Fund Project) SQ2024162National Key Science and Technology Project Undertaken by Shenzhen CJGJZD20220517142405013Ningbo International Cooperation project 2023H014S&T Program of Qinhuangdao 202401A049
6 · The paper itself

Abstract

In rehabilitation, physicians plan lower-limb exercises via linear guidance. Ensuring efficacy and safety, they design patient-specific paths, carefully plotting smooth trajectories to minimize jerks. Replicating their precision in robotics is a major challenge. This study introduces a linear rehabilitation motion planning method designed for physicians to use a multi-posture lower-limb rehabilitation robot, encompassing both path and trajectory planning. By subdividing the lower limb's action space into four distinct training sections and classifying this space, we articulate the correlation between linear trajectories and key joint rehabilitation metrics. Building upon this foundation, a rehabilitative path generation system is developed, anchored in joint rehabilitation indicators. Subsequently, high-order polynomial curves are employed to mimic the smooth continuity of traditional rehabilitation trajectories and joint motions. Furthermore, trajectory planning is refined through the resolution of a constrained quadratic optimization problem, aiming to minimize the abrupt jerks in the trajectory. The optimized trajectories derived from our experiments are compared with randomly generated trajectories, demonstrating the suitability of trajectory optimization for real-time rehabilitation trajectory planning. Additionally, we compare trajectories generated based on the two groups of joint rehabilitation indicators, indicating that the proposed path generation system effectively assists clinicians in executing efficient and precise robot-assisted rehabilitation path planning.

Indexed as

Lower ExtremityRoboticsAlgorithmsBiomechanical PhenomenaExercise TherapyHumansMotionRehabilitationhigh-order polynomial curvesjoint rehabilitationminimized jerkmulti-posture lower-limb rehabilitation robotrehabilitative motion planning

Identifiers

PMID39686042
PMCPMC11644415

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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.