Evidence map›Paper›PMID 35888970›Full record

ArticleMicromachines2022

Cooperativity Model for Improving the Walking-Assistance Efficiency of the Exoskeleton.

Jianfeng Ma, Decheng Sun, Yongqing Ding, Daihe Luo, Xiao Chen

Abstract read
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

5 authors.

Jianfeng MaDepartment of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0001-6786-1292
Decheng SunDepartment of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Yongqing DingDepartment of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Daihe LuoDepartment of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, China.
Xiao ChenSystems Engineering Institute, Academy of Military Science, Beijing 100010, China.

Funding

National Natural Science Foundation of China 51575007
6 · The paper itself

Abstract

(1) Background: To enhance the walking-assistance efficiencies of exoskeletons, this paper proposed the biomechanical-based cooperativity model based on a passive exoskeleton prototype to fill the technical gap in exoskeleton design regarding the torque transmission law between humans and exoskeletons. (2) Methods: The cooperativity model was used to solve the key system parameters based on the minimum average dispersion degree, in which the average dispersion degree algorithm based on the joint angle was designed and applied. (3) Results: The influence of the cooperativity model on the exoskeleton was indicated by comparing the walking-assistance efficiencies of the exoskeletons with the same structure but with different elastic parameters of the energy storage components, in which the exoskeleton based on the cooperativity design exhibited the highest walking-assistance performance. The walking-assistance efficiency of the exoskeleton with the optimal parameter combinations was also tested by comparing the respiratory metabolisms with and without the exoskeleton, in which the exoskeleton provided the average walking-assistance efficiency of 14.45% for more than 80% of the subjects. (4) Conclusions: The effects of the cooperativity model on exoskeletons were proven, but the accuracy and efficiency of the model still have room for improvement, especially the accuracy of the offset principle.

Indexed as

cooperativity modeldispersion degree algorithmenergy conversionoffset theorypassive walking-assistance exoskeleton

Identifiers

PMID35888970
PMCPMC9323024

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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.