Evidence map›Paper›PMID 35161507›Full record

ArticleSensors (Basel, Switzerland)2022

Neural Network-Enabled Flexible Pressure and Temperature Sensor with Honeycomb-like Architecture for Voice Recognition.

Yue Su, Kainan Ma, Xu Zhang, Ming Liu

Abstract 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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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.

Yue SuInstitute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
Kainan MaInstitute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.ORCID 0000-0002-1379-591X
Xu ZhangInstitute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.
Ming LiuInstitute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.

Funding

Key Research Program of Frontier Science, Chinese Academy of Sciences QYZDY-SSW-JSC004
6 · The paper itself

Abstract

Flexible pressure sensors have been studied as wearable voice-recognition devices to be utilized in human-machine interaction. However, the development of highly sensitive, skin-attachable, and comfortable sensing devices to achieve clear voice detection remains a considerable challenge. Herein, we present a wearable and flexible pressure and temperature sensor with a sensitive response to vibration, which can accurately recognize the human voice by combing with the artificial neural network. The device consists of a polyethylene terephthalate (PET) printed with a silver electrode, a filament-microstructured polydimethylsiloxane (PDMS) film embedded with single-walled carbon nanotubes and a polyimide (PI) film sputtered with a patterned Ti/Pt thermistor strip. The developed pressure sensor exhibited a pressure sensitivity of 0.398 kPa-1 in the low-pressure regime, and the fabricated temperature sensor shows a desirable temperature coefficient of resistance of 0.13% ∘C in the range of 25 ∘C to 105 ∘C. Through training and testing the neural network model with the waveform data of the sensor obtained from human pronunciation, the vocal fold vibrations of different words can be successfully recognized, and the total recognition accuracy rate can reach 93.4%. Our results suggest that the fabricated sensor has substantial potential for application in the human-computer interface fields, such as voice control, vocal healthcare monitoring, and voice authentication.

Indexed as

Nanotubes, CarbonWearable Electronic DevicesHumansNeural Networks, ComputerTemperatureVoice RecognitionNanotubes, Carbonartificial neural networkfilament-microstructuredflexible pressure and temperature sensorvoice-recognition

Identifiers

PMID35161507
PMCPMC8840666

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.