Evidence map›Paper›PMID 33198141›Full record

ArticleSensors (Basel, Switzerland)2020

A Low-Cost Calibration Method for Low-Cost MEMS Accelerometers Based on 3D Printing.

Jesús A García, Evangelina Lara, Leocundo Aguilar

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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. 3D printing of micro-nano devices and their applications.Microsystems & nanoengineering · 2025
    Review
  3. Article
  4. 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

3 authors.

Jesús A GarcíaFacultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Tijuana BC 22390, Mexico.
Evangelina LaraFacultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Tijuana BC 22390, Mexico.
Leocundo AguilarFacultad de Ciencias Químicas e Ingeniería, Universidad Autónoma de Baja California, Tijuana BC 22390, Mexico.

Funding

Consejo Nacional de Ciencia y Tecnología 383573
6 · The paper itself

Abstract

A ubiquitous sensor in embedded systems is the accelerometer, as it enables a range of applications. However, accelerometers experience nonlinearities in their outputs caused by error terms and axes misalignment. These errors are a major concern because, in applications such as navigations systems, they accumulate over time, degrading the position accuracy. Through a calibration procedure, the errors can be modeled and compensated. Many methods have been proposed; however, they require sophisticated equipment available only in laboratories, which makes them complex and expensive. In this article, a simple, practical, and low-cost calibration method is proposed. It uses a 3D printed polyhedron, benefiting from the popularisation and low-cost of 3D printing in the present day. Additionally, each polyhedron could hold as much as 14 sensors, which can be calibrated simultaneously. The method was performed with a low-cost sensor and it significantly reduced the root-mean-square error (RMSE) of the sensor output. The RMSE was compared with the reported in similar proposals, and our method resulted in higher performance. The proposal enables accelerometer calibration at low-cost, and anywhere and anytime, not only by experts in laboratories. Compensating the sensor's inherent errors thus increases the accuracy of its output.

Indexed as

Micro-Electrical-Mechanical SystemsPrinting, Three-DimensionalAccelerometryCalibration3D printaccelerometercalibrationinertial sensorlow-costmems

Identifiers

PMID33198141
PMCPMC7697012

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.