Evidence map›Paper›PMID 39483202›Full record

ArticleIEEE transactions on microwave theory and techniques2021

A Wireless Artificial Mechanoreceptor in 180-nm CMOS.

Han Hao, Lin Du, Andrew G Richardson, Timothy H Lucas, Mark G Allen, Jan Van der Spiegel, Firooz Aflatouni

Abstract read
In one paragraph

Article in IEEE transactions on microwave theory and techniques, 2021. 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. Review
  2. 17.10 A 0.4V, 750nW, Individually Accessible Wireless Capacitive Sensor Interface IC for a Tactile Sensing Network.Digest of technical papers. IEEE International Solid-State Circuits Conference · 2024
    Article
  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

7 authors.

Han HaoDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
Lin DuDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
Andrew G RichardsonDepartment of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104 USA.
Timothy H LucasDepartment of Neurosurgery, University of Pennsylvania, Philadelphia, PA 19104 USA.
Mark G AllenDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
Jan Van der SpiegelDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.
Firooz AflatouniDepartment of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA.

Funding

An Implantable Wireless Tactile Feedback SystemR01NS107550 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI LUCAS, TIMOTHY H · 2019 to 2022
$2.4M
NINDS NIH HHS R01 NS107550
6 · The paper itself

Abstract

This article presents an implantable low-power wireless integrated system for tactile sensing applications. The reported ASIC utilizes a low-loss magnetic human body communication channel for both wireless power and data transfer. The chip is hybrid-integrated with an in-house fabricated MEMS capacitive force sensor to form an implantable artificial mechanoreceptor. An on-chip correlated double sampling capacitance to time converter consumes 750nW from a 1.2V on-chip regulated supply, achieving a 2.0fF resolution for an input capacitance of 14pF and a FoM of 49 fJ/c-s while occupying an area of only 0.04 mm

Indexed as

Body channel communication (BCC)capacitance to time converter (CTC)correlated double sampling (CDS)energy efficiencymagnetic human body communication (mHBC)power managementsensor interface

Identifiers

PMID39483202
PMCPMC11526849

What OpenQuestion holds

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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.