Evidence map›Paper›PMID 36554067›Full record

ArticleHealthcare (Basel, Switzerland)2022

Development of a Real-Time Knee Extension Monitoring and Rehabilitation System: Range of Motion and Surface EMG Measurement and Evaluation.

Kiattisak Sengchuai, Chinnakrit Kanjanaroat, Jermphiphut Jaruenpunyasak, Chonnanid Limsakul, Watcharin Tayati, Apidet Booranawong, Nattha Jindapetch

Open access · goldAbstract read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.2field-weighted citation impact, top 23% 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.

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 synthesis or guideline pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Review
  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

7 authors at 1 institution in 1 country.

Kiattisak SengchuaiDepartment of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90110, Thailand.
Chinnakrit KanjanaroatDepartment of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90110, Thailand.
Jermphiphut JaruenpunyasakDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0002-7264-5946
Chonnanid LimsakulDepartment of Rehabilitation Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla 90110, Thailand.
Watcharin TayatiPhysical Therapy Unit, Department of Rehabilitation, Trang Hospital, Trang 92000, Thailand.
Apidet BooranawongDepartment of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90110, Thailand.ORCID 0000-0002-5346-1594
Nattha JindapetchDepartment of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla 90110, Thailand.
Prince of Songkla University · TH

Funding

Broadcasting and Telecommunications Research and Development Fund for Public Interest, Thailand D62-0-(2)-001
6 · The paper itself

Abstract

In this paper, a real-time knee extension monitoring and rehabilitation system for people, such as patients, the elderly, athletes, etc., is developed and tested. The proposed system has three major functions. The first function is two-channel surface electromyography (EMG) signal measurement and processing for the vastus lateralis (VL) and vastus medialis (VM) muscles using a developed EMG device set. The second function is the knee extension range of motion (ROM) measurement using an angle sensor device set (i.e., accelerometer sensor). Both functions are connected and parallelly processed by the NI-myRIO embedded device. Finally, the third function is the graphical user interface (GUI) using LabVIEW, where the knee rehabilitation program can be defined and flexibly set, as recommended by physical therapists and physicians. Experimental results obtained from six healthy subjects demonstrated that the proposed system can efficiently work with real-time response. It can support multiple rehabilitation users with data collection, where EMG signals with mean absolute value (MAV) and root mean square value (RMS) results and knee extension ROM data can be automatically measured and recorded based on the defined rehabilitation program. Furthermore, the proposed system is also employed in the hospital for validation and evaluation, where bio-feedback EMG and ROM data from six patients, including (a) knee osteoarthritis, (b) herniated disc, (c) knee ligament injury, (d) ischemic stroke, (e) hemorrhagic stroke, and (f) Parkinson are obtained. Such data are also collected for one month for tracking, evaluation, and treatment. With our proposed system, results indicate that the rehabilitation people can practice themselves and know their rehabilitation progress during the time of testing. The system can also evaluate (as a primary treatment) whether the therapy training is successful or not, while experts can simultaneously review the progress and set the optimal treatment program in response to the rehabilitation users. This technology can also be integrated as a part of the Internet of Things (IoT) and smart healthcare systems.

Indexed as

knee extensionreal-time monitoringrehabilitationROMsurface EMG

Identifiers

PMID36554067
PMCPMC9778223
OpenAlexW4312140950

What OpenQuestion 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 OpenQuestion graph.