Evidence map›Paper›PMID 32759646›Full record

ArticleSensors (Basel, Switzerland)2020

Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance.

Chunjie Chen, Yu Zhang, Yanjie Li, Zhuo Wang, Yida Liu, Wujing Cao, Xinyu Wu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
–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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

7 authors.

Chunjie ChenCAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.ORCID 0000-0003-4855-9914
Yu ZhangCAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.
Yanjie LiHarbin Institute of Technology, School of Mechanical Engineering and Automation, Shenzhen 518055, China.
Zhuo WangCAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.ORCID 0000-0002-1709-7764
Yida LiuCAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.
Wujing CaoCAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.
Xinyu WuCAS Key Laboratory of Human-Machine-Intelligence Synergic Systems, Shenzhen Institutes of Advanced Technology, Shenzhen 518055, China.

Funding

Guangdong Basic and Applied Basic Research Foundation 2019A1515110576National Key Research and Development Program of China 2018YFB130710National Natural Science Foundation U1813206, 61977019Natural Science Foundation of Guangdong Province, China 2019A1515010782Science, Technology and Innovation Committee of Shenzhen Municipality (SZSTI) Fundamental Research and Discipline Layout Project JCYJ20180302145539583Shenzhen basic research program JCYJ20180507183837726,JCYJ20170811155028832Shenzhen Institute of Artificial Intelligence and Robotics for Society, National Key Research and Development Program of China 2018YFB130710Shenzhen Technology Research Project JSGG20180507182901552
6 · The paper itself

Abstract

Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multiple joints in the lower limb is presented in this paper. It is used to assist both hip and knee joints in a single system, the assistance force is directly applied to the hip joint flexion and the knee joint extension, while indirectly to the hip extension also. Based on the biological torque of human walking at three different slopes, a novel strategy is developed to improve the performance of assistance. A parameter optimal iterative learning control (POILC) method is introduced to reduce the error generated due to the difference between the wearing position and the biological features of the different wearers. In order to obtain the metabolic rate, three subjects walked on a treadmill, for 10 min on each terrain, at a speed of 4 km/h under both conditions of wearing and not wearing the soft exoskeleton. Results showed that the metabolic rate was decreased with the increasing slope of the terrain. The reductions in the net metabolic rate in the experiments on the downhill, flat ground, and uphill were, respectively, 9.86%, 12.48%, and 22.08% compared to the condition of not wearing the soft exoskeleton, where their corresponding absolute values were 0.28 W/kg, 0.72 W/kg, and 1.60 W/kg.

Indexed as

Exoskeleton DeviceMachine LearningWalkingBiomechanical PhenomenaEnergy MetabolismGaitHip JointHumansKnee JointTorqueforce trackingiterative learning controllower limb assistancemetabolic costsoft exoskeleton

Identifiers

PMID32759646
PMCPMC7435451

What OpenQuestion holds

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Registered trials

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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.