Evidence map›Paper›PMID 29065677›Full record

ArticleJournal of healthcare engineering2017

Developing a Low-Cost Force Treadmill via Dynamic Modeling.

Chih-Yuan Hong, Lan-Yuen Guo, Rong Song, Mark L Nagurka, Jia-Li Sung, Chen-Wen Yen

Open access · hybridAbstract read
In one paragraph

Article in Journal of healthcare engineering, 2017. 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
0.6field-weighted citation impact, top 24% 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, 4 citations in OpenAlex.

  1. Article
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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

6 authors at 4 institutions in 3 countries.

Chih-Yuan HongDepartment of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
Lan-Yuen GuoDepartment of Sports Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.ORCID 0000-0002-6256-355X
Rong SongSchool of Engineering, Sun Yat-sen University, Guangzhou, China.ORCID 0000-0003-3662-116X
Mark L NagurkaDepartment of Mechanical Engineering, Marquette University, Milwaukee, WI, USA.ORCID 0000-0001-6331-905X
Jia-Li SungDepartment of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.
Chen-Wen YenDepartment of Mechanical and Electromechanical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan.ORCID 0000-0002-4894-7055
National Sun Yat-sen University · TWKaohsiung Medical University · TWMarquette University · USSun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

By incorporating force transducers into treadmills, force platform-instrumented treadmills (commonly called force treadmills) can collect large amounts of gait data and enable the ground reaction force (GRF) to be calculated. However, the high cost of force treadmills has limited their adoption. This paper proposes a low-cost force treadmill system with force sensors installed underneath a standard exercise treadmill. It identifies and compensates for the force transmission dynamics from the actual GRF applied on the treadmill track surface to the force transmitted to the force sensors underneath the treadmill body. This study also proposes a testing procedure to assess the GRF measurement accuracy of force treadmills. Using this procedure in estimating the GRF of "walk-on-the-spot motion," it was found that the total harmonic distortion of the tested force treadmill system was about 1.69%, demonstrating the effectiveness of the approach.

Indexed as

RunningWalkingAlgorithmsEquipment DesignExercise Test

Identifiers

PMID29065677
PMCPMC5474287
OpenAlexW2620651220

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.