Evidence map›Paper›PMID 35032113›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2022

Self-Powered Artificial Mechanoreceptor Based on Triboelectrification for a Neuromorphic Tactile System.

Joon-Kyu Han, Il-Woong Tcho, Seung-Bae Jeon, Ji-Man Yu, Weon-Guk Kim, Yang-Kyu Choi

Erratum issuedAbstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Review
  2. Self-Powered Neuromorphic Systems Based on Tribotronics Synaptic Devices.Small (Weinheim an der Bergstrasse, Germany) · 2026
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Joon-Kyu HanSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
Il-Woong TchoSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
Seung-Bae JeonElectronics Engineering Department, Hanbat National University, 125 Dongseo-daero, Yuseong-gu, Daejeon, 34158, Republic of Korea.
Ji-Man YuSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
Weon-Guk KimSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.
Yang-Kyu ChoiSchool of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, Republic of Korea.ORCID 0000-0001-5480-7027

Funding

National Research Foundation of Korea 2018R1A2A3075302National Research Foundation of Korea 2019M3F3A1A03079603National Research Foundation of Korea 2020M3F3A2A01082592
6 · The paper itself

Abstract

A self-powered artificial mechanoreceptor module is demonstrated with a triboelectric nanogenerator (TENG) as a pressure sensor with sustainable energy harvesting and a biristor as a neuron. By mimicking a biological mechanoreceptor, it simultaneously detects the pressure and encodes spike signals to act as an input neuron of a spiking neural network (SNN). A self-powered neuromorphic tactile system composed of artificial mechanoreceptor modules with an energy harvester can greatly reduce the power consumption compared to the conventional tactile system based on von Neumann computing, as the artificial mechanoreceptor module itself does not demand an external energy source and information is transmitted with spikes in a SNN. In addition, the system can detect low pressures near 3 kPa due to the high output range of the TENG. It therefore can be advantageously applied to robotics, prosthetics, and medical and healthcare devices, which demand low energy consumption and low-pressure detection levels. For practical applications of the neuromorphic tactile system, classification of handwritten digits is demonstrated with a software-based simulation. Furthermore, a fully hardware-based breath-monitoring system is implemented using artificial mechanoreceptor modules capable of detecting wind pressure of exhalation in the case of pulmonary respiration and bending pressure in the case of abdominal breathing.

Indexed as

RoboticsTouchMechanoreceptorsMonitoring, PhysiologicNeural Networks, Computerbiristor neuronbreath monitoringmechanoreceptorsspiking neural networktriboelectric nanogenerators

Identifiers

PMID35032113
PMCPMC8948587

What OpenQuestion holds

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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.