Evidence map›Paper›PMID 35098136›Full record

ArticleACS applied electronic materials2022

Skin-Inspired Flexible and Stretchable Electrospun Carbon Nanofiber Sensors for Neuromorphic Sensing.

Debarun Sengupta, Michele Mastella, Elisabetta Chicca, Ajay Giri Prakash Kottapalli

Open access · hybridAbstract read
In one paragraph

Article in ACS applied electronic materials, 2022. 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
1.9field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 25 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  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

4 authors at 1 institution in 2 countries.

Debarun SenguptaDepartment of Advanced Production Engineering (APE), Engineering and Technology Institute Groningen (ENTEG), University of Groningen, Groningen 9747 AG, The Netherlands.ORCID https://orcid.org/0000-0002-3500-8585
Michele MastellaGroningen Cognitive Systems and Materials Center (CogniGron), University of Groningen, Groningen 9747 AG, The Netherlands.
Elisabetta ChiccaGroningen Cognitive Systems and Materials Center (CogniGron), University of Groningen, Groningen 9747 AG, The Netherlands.
Ajay Giri Prakash KottapalliDepartment of Advanced Production Engineering (APE), Engineering and Technology Institute Groningen (ENTEG), University of Groningen, Groningen 9747 AG, The Netherlands.ORCID https://orcid.org/0000-0002-3868-7069
University of Groningen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the past few decades, a significant amount of research effort has been dedicated toward developing skin-inspired sensors for real-time human motion monitoring and next-generation robotic devices. Although several flexible and wearable sensors have been developed in the past, the need of the hour is developing accurate, reliable, sophisticated, facile yet inexpensive flexible sensors coupled with neuromorphic systems or spiking neural networks to encode tactile information without the need for complex digital architectures, thus achieving true skin-like sensing with limited resources. In this work, we propose an approach entailing carbon nanofiber-polydimethylsiloxane composite-based piezoresistive sensors, coupled with spiking neural networks, to mimic skin-like sensing. The strain and pressure sensors have been combined with appropriately designed neural networks to encode analog voltages to spikes to recreate bioinspired tactile sensing and proprioception. To further validate the proprioceptive capability of the system, a gesture tracking smart glove, combined with a spiking neural network, was demonstrated. Wearable and flexible sensors with accompanying neural networks such as the ones proposed in this work will pave the way for a future generation of skin-mimetic sensors for advanced prosthetic devices, apparel integrable smart sensors for human motion monitoring, and human-machine interfaces.

Identifiers

PMID35098136
PMCPMC8793024
OpenAlexW4206575091

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.