Evidence map›Paper›PMID 31783618›Full record

ArticleSensors (Basel, Switzerland)2019

Flexible Insole Sensors with Stably Connected Electrodes for Gait Phase Detection.

Wenzheng Heng, Gaoyang Pang, Feihong Xu, Xiaoyan Huang, Zhibo Pang, Geng Yang

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Wenzheng HengState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.
Gaoyang PangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.ORCID 0000-0002-0948-4641
Feihong XuInstitute of Mechanical Manufacturing Technology, China Academy of Engineering Physics, Mianyang 621900, China.
Xiaoyan HuangCollege of Electrical Engineering, Zhejiang University, Hangzhou 310027, China.
Zhibo PangABB Corporate Research Sweden, 72178 Vasteras, Sweden.ORCID 0000-0002-7474-4294
Geng YangState Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China.ORCID 0000-0002-8685-5426

Funding

The China's Thousand Talents Plan Young Professionals Program, Robotics Institute of Zhejiang University K18-508116-008-03The Major Research Plan of National Natural Science Foundation of China 51890884The National Natural Science Foundation of China 51975513The Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems GZKF-201703 GZKF-201703The Science Fund for Creative Research Groups of the National Natural Science Foundation of China 51821093
6 · The paper itself

Abstract

Gait analysis is an important assessment tool for analyzing vital signals collected from individuals and for providing physical information of the human body, and it is emerging in a diverse range of application scenarios, such as disease diagnosis, fall prevention, rehabilitation, and human-robot interaction. Herein, a kind of surface processed conductive rubber was designed and investigated to develop a pressure-sensitive insole to monitor planar pressure in a real-time manner. Due to a novel surface processing method, the pressure sensor was characterized by stable contact resistance, simple manufacturing, and high mechanical durability. In the experiments, it was demonstrated that the developed pressure sensors were easily assembled with the inkjet-printed electrodes and a flexible substrate as a pressure-sensitive insole while maintaining good sensing performance. Moreover, resistive signals were wirelessly transmitted to computers in real time. By analyzing sampled resistive data combined with the gait information monitored by a visual-based reference system based on machine learning method (k-Nearest Neighbor algorithm), the corresponding relationship between plantar pressure distribution and lower limb joint angles was obtained. Finally, the experimental validation of the ability to accurately divide gait into several phases was conducted, illustrating the potential application of the developed device in healthcare and robotics.

Indexed as

gait phaseinkjet printinginsole sensorsmachine learningsurface processingvisual-based reference

Identifiers

PMID31783618
PMCPMC6928953

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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.