Evidence map›Paper›PMID 38475089›Full record

ArticleSensors (Basel, Switzerland)2024

Multisensor Integrated Platform Based on MEMS Charge Variation Sensing Technology for Biopotential Acquisition.

Fernanda Irrera, Alessandro Gumiero, Alessandro Zampogna, Federico Boscari, Angelo Avogaro, Michele Antonio Gazzanti Pugliese di Cotrone, Martina Patera, Luigi Della Torre, Nicola Picozzi, Antonio Suppa

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Fernanda IrreraDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Rome, Italy.ORCID 0000-0003-1831-6786
Alessandro GumieroSTMicroelectronics Agrate, 20864 Agrate Brianza, Italy.
Alessandro ZampognaDepartment of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy.ORCID 0000-0002-0227-1993
Federico BoscariDepartment of Medicine, University of Padua, 35122 Padua, Italy.
Angelo AvogaroDepartment of Medicine, University of Padua, 35122 Padua, Italy.
Michele Antonio Gazzanti Pugliese di CotroneDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Rome, Italy.
Martina PateraDepartment of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy.
Luigi Della TorreSTMicroelectronics Agrate, 20864 Agrate Brianza, Italy.
Nicola PicozziSTMicroelectronics Agrate, 20864 Agrate Brianza, Italy.
Antonio SuppaDepartment of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy.ORCID 0000-0001-9903-5550

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a new methodology for long-term biopotential recording based on an MEMS multisensor integrated platform featuring a commercial electrostatic charge-transfer sensor. This family of sensors was originally intended for presence tracking in the automotive industry, so the existing setup was engineered for the acquisition of electrocardiograms, electroencephalograms, electrooculograms, and electromyography, designing a dedicated front-end and writing proper firmware for the specific application. Systematic tests on controls and nocturnal acquisitions from patients in a domestic environment will be discussed in detail. The excellent results indicate that this technology can provide a low-power, unexplored solution to biopotential acquisition. The technological breakthrough is in that it enables adding this type of functionality to existing MEMS boards at near-zero additional power consumption. For these reasons, it opens up additional possibilities for wearable sensors and strengthens the role of MEMS technology in medical wearables for the long-term synchronous acquisition of a wide range of signals.

Indexed as

Micro-Electrical-Mechanical SystemsElectrocardiographyElectroencephalographyElectromyographyHumansTechnologycharge variation sensorslong-term biopotential recordinglow power consumptionMEMS technologywearable sensors

Identifiers

PMID38475089
PMCPMC10934124

What Socratic 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 Socratic graph.