Evidence map›Paper›PMID 41590272›Full record

ReviewBiosensors2025

Active Rehabilitation Technologies for Post-Stroke Patients.

Hongbei Meng, Zihe Zhao, Shangru Li, Shengbo Wang, Jiacheng Wang, Canxi Yang, Chenyu Tang, Xuhang Chen, Xiaoxue Zhai, Yu Pan and 4 more

Abstract readReview
In one paragraph

Review in Biosensors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
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

14 authors.

Hongbei MengSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID 0009-0002-7769-6926
Zihe ZhaoSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Shangru LiSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Shengbo WangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.ORCID 0000-0003-1212-138X
Jiacheng WangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Canxi YangSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.
Chenyu TangDepartment of Engineering, University of Cambridge, Cambridge CB3 0FA, UK.ORCID 0000-0002-6368-5639
Xuhang ChenBrain Physics Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK.
Xiaoxue ZhaiDepartment of Physical Medicine and Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing 100084, China.
Yu PanDepartment of Physical Medicine and Rehabilitation, Beijing Tsinghua Changgung Hospital, Beijing 100084, China.
Arokia NathanDepartment of Engineering, Darwin College, University of Cambridge, Cambridge CB3 9EU, UK.ORCID 0000-0002-2070-8853
Peter SmielewskiWolfson Brain Imaging Centre, Addenbrooke's Hospital, Cambridge CB3 0FA, UK.ORCID 0000-0001-5096-3938
Luigi G OcchipintiDepartment of Engineering, University of Cambridge, Cambridge CB3 0FA, UK.ORCID 0000-0002-9067-2534
Shuo GaoSchool of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Funding

Beijing·Natural·Science Foundation 305QYJH2025117002UK Engineering and Physical Sciences Research Council EP/K03099X/1UK Engineering and Physical Sciences Research Council EP/L015889/1UK Engineering and Physical Sciences Research Council EP/L016087/1UK Engineering and Physical Sciences Research Council EP/P027628/1UK Engineering and Physical Sciences Research Council EP/W024284/1UKRI Centre for Doctoral Training in AI for Healthcare EP/S023283/1
6 · The paper itself

Abstract

Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active rehabilitation methods can respond to patients' motor intentions in real-time and significantly increase motivation and engagement, leading to efficient utilization of critical recovery windows and better rehabilitation outcomes. In this review, we focus on the physiological basis of active rehabilitation, including mechanisms of neuroplasticity, and discuss recent advances in intent detection and feedback devices. We also examine treatment options for different stages of stroke recovery, providing a comprehensive reference for engineers to design optimized rehabilitation techniques and for clinicians to select appropriate rehabilitation protocols. These developments create new opportunities to improve the lives of stroke patients and offer greater hope for their recovery.

Indexed as

StrokeStroke RehabilitationAnimalsEquipment DesignHumansMotionNeuronal PlasticityRecovery of FunctionVirtual Realityactive rehabilitationclinical practicefeedback interventionintention recognitionneuroplasticitystroke

Identifiers

PMID41590272
PMCPMC12839297

What Socratic holds

Textmetadata
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 Socratic graph.