Evidence map›Paper›PMID 36560095›Full record

ArticleSensors (Basel, Switzerland)2022

A 3D-Printed Capacitive Smart Insole for Plantar Pressure Monitoring.

Anastasios G Samarentsis, Georgios Makris, Sofia Spinthaki, Georgios Christodoulakis, Manolis Tsiknakis, Alexandros K Pantazis

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.7field-weighted citation impact, top 10% 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

9 citing papers in PubMed, 34 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Static and dynamic optimisation of fluid-filled responsive orthotic insoles.Biomechanics and modeling in mechanobiology · 2025
    Article
  6. Article
  7. 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 3 institutions in 1 country.

Anastasios G SamarentsisInstitute of Electronic Structure and Laser, Foundation for Research and Technology Hellas, 70013 Heraklion, Greece.ORCID 0000-0002-8154-1938
Georgios MakrisInstitute of Electronic Structure and Laser, Foundation for Research and Technology Hellas, 70013 Heraklion, Greece.
Sofia SpinthakiDepartment of Physics, University of Crete, 70013 Heraklion, Greece.
Georgios ChristodoulakisDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece.
Manolis TsiknakisDepartment of Electrical and Computer Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece.ORCID 0000-0001-8454-1450
Alexandros K PantazisInstitute of Electronic Structure and Laser, Foundation for Research and Technology Hellas, 70013 Heraklion, Greece.
Foundation for Research and Technology Hellas · GRHellenic Mediterranean University · GRUniversity of Crete · GR

Funding

European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH - CREATE - INNOVATE T1EDK-01888
6 · The paper itself

Abstract

Gait analysis refers to the systematic study of human locomotion and finds numerous applications in the fields of clinical monitoring, rehabilitation, sports science and robotics. Wearable sensors for real-time gait monitoring have emerged as an attractive alternative to the traditional clinical-based techniques, owing to their low cost and portability. In addition, 3D printing technology has recently drawn increased interest for the manufacturing of sensors, considering the advantages of diminished fabrication cost and time. In this study, we report the development of a 3D-printed capacitive smart insole for the measurement of plantar pressure. Initially, a novel 3D-printed capacitive pressure sensor was fabricated and its sensing performance was evaluated. The sensor exhibited a sensitivity of 1.19 MPa−1, a wide working pressure range (<872.4 kPa), excellent stability and durability (at least 2.280 cycles), great linearity (R2=0.993), fast response/recovery time (142−160 ms), low hysteresis (DH<10%) and the ability to support a broad spectrum of gait speeds (30−70 steps/min). Subsequently, 16 pressure sensors were integrated into a 3D-printed smart insole that was successfully applied for dynamic plantar pressure mapping and proven able to distinguish the various gait phases. We consider that the smart insole presented here is a simple, easy to manufacture and cost-effective solution with the potential for real-world applications.

Indexed as

GaitGait AnalysisHumansPressurePrinting, Three-DimensionalShoes3D printingcapacitive pressure sensorsgait analysisreal-time plantar pressure monitoringsmart insolewearable sensors

Identifiers

PMID36560095
PMCPMC9782173
OpenAlexW4312127171

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.