Evidence map›Paper›PMID 28446181›Full record

ArticleJournal of neuroengineering and rehabilitation2017

A Neuromuscular Electrical Stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke.

Wei Rong, Waiming Li, Mankit Pang, Junyan Hu, Xijun Wei, Bibo Yang, Honwah Wai, Xiaoxiang Zheng, Xiaoling Hu

Registry-linked trialOpen access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02117089 (Biomechatronic System Using Voluntary Motor Effort), which is not on this map. Cited by 27 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 4 pooled it
6.3field-weighted citation impact, top 3% of its field
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.

NCT02117089 naunknown statusnot on this map

Biomechatronic System Using Voluntary Motor Effort (VME)-Driven Neuromuscular Electrical Stimulation (NMES) for Upper Limb Rehabilitation

TypeinterventionalSponsorThe Hong Kong Polytechnic UniversityRan2013 to 2025Enrolled48ConditionsStrokeArmsElectrical Stimulation, Rehabilitation Robot
3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 4 syntheses or guidelines pooled it, 68 citations in OpenAlex.

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  20. [Research advances on functional training robots in burn rehabilitation].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2022
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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 at 2 institutions in 2 countries.

Wei RongInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Waiming LiInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Mankit PangIndustry Centre, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Junyan HuInstitute of Textile & Clothing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Xijun WeiInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Bibo YangThe Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Honwah WaiIndustry Centre, The Hong Kong Polytechnic University, Hung Hom, Hong Kong.
Xiaoxiang ZhengQiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China.
Xiaoling HuInterdisciplinary Division of Biomedical Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong. Xiaoling.Hu@polyu.edu.hk.ORCID 0000-0003-3188-3005
Hong Kong Polytechnic University · HKZhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIt is a challenge to reduce the muscular discoordination in the paretic upper limb after stroke in the traditional rehabilitation programs.

methodIn this study, a neuromuscular electrical stimulation (NMES) and robot hybrid system was developed for multi-joint coordinated upper limb physical training. The system could assist the elbow, wrist and fingers to conduct arm reaching out, hand opening/grasping and arm withdrawing by tracking an indicative moving cursor on the screen of a computer, with the support from the joint motors and electrical stimulations on target muscles, under the voluntary intention control by electromyography (EMG). Subjects with chronic stroke (n = 11) were recruited for the investigation on the assistive capability of the NMES-robot and the evaluation of the rehabilitation effectiveness through a 20-session device assisted upper limb training.

resultsIn the evaluation, the movement accuracy measured by the root mean squared error (RMSE) during the tracking was significantly improved with the support from both the robot and NMES, in comparison with those without the assistance from the system (P < 0.05). The intra-joint and inter-joint muscular co-contractions measured by EMG were significantly released when the NMES was applied to the agonist muscles in the different phases of the limb motion (P < 0.05). After the physical training, significant improvements (P < 0.05) were captured by the clinical scores, i.e., Modified Ashworth Score (MAS, the elbow and the wrist), Fugl-Meyer Assessment (FMA), Action Research Arm Test (ARAT), and Wolf Motor Function Test (WMFT).

conclusionsThe EMG-driven NMES-robotic system could improve the muscular coordination at the elbow, wrist and fingers.

trial registrationClinicalTrials.gov. NCT02117089 ; date of registration: April 10, 2014.

Indexed as

Electric StimulationExoskeleton DeviceMuscle, SkeletalRoboticsUpper ExtremityAdultAgedChronic DiseaseElbow JointFemaleFinger JointHumansMaleMiddle AgedParesisPhysical Education and TrainingNeuromuscular electrical stimulationRobotStroke

Identifiers

PMID28446181
PMCPMC5406922
OpenAlexW2608047598

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.