Evidence map›Paper›PMID 33093670›Full record

ArticleNature biomedical engineering2020

Decoding of facial strains via conformable piezoelectric interfaces.

Tao Sun, Farita Tasnim, Rachel T McIntosh, Nikta Amiri, Dana Solav, Mostafa Tavakkoli Anbarani, David Sadat, Lin Zhang, Yuandong Gu, M Amin Karami and 1 more

Abstract readVideo-Audio Media
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In one paragraph

Article in Nature biomedical engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

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  11. Article
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  13. Artificial Intelligence-Powered Electronic Skin.Nature machine intelligence · 2023
    Article
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  15. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Tao Sun *Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Farita Tasnim *Media Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-1215-1290
Rachel T McIntoshMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nikta AmiriDepartment of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA.
Dana SolavMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Mostafa Tavakkoli AnbaraniDepartment of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA.
David SadatMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Lin ZhangMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Yuandong GuInstitute of Microelectronics, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.
M Amin KaramiDepartment of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, NY, USA.
Canan DagdevirenMedia Lab, Massachusetts Institute of Technology, Cambridge, MA, USA. canand@media.mit.edu.ORCID http://orcid.org/0000-0002-2032-792X

Funding

Development of energy harvesters for powering leadless pacemakers from myocardial motionR21EB023613 · NIBIB · STATE UNIVERSITY OF NEW YORK AT BUFFALO · PI KARAMI, M. AMIN · 2017 to 2018
$403k
6 · The paper itself

Abstract

Devices that facilitate nonverbal communication typically require high computational loads or have rigid and bulky form factors that are unsuitable for use on the face or on other curvilinear body surfaces. Here, we report the design and pilot testing of an integrated system for decoding facial strains and for predicting facial kinematics. The system consists of mass-manufacturable, conformable piezoelectric thin films for strain mapping; multiphysics modelling for analysing the nonlinear mechanical interactions between the conformable device and the epidermis; and three-dimensional digital image correlation for reconstructing soft-tissue surfaces under dynamic deformations as well as for informing device design and placement. In healthy individuals and in patients with amyotrophic lateral sclerosis, we show that the piezoelectric thin films, coupled with algorithms for the real-time detection and classification of distinct skin-deformation signatures, enable the reliable decoding of facial movements. The integrated system could be adapted for use in clinical settings as a nonverbal communication technology or for use in the monitoring of neuromuscular conditions.

Indexed as

AlgorithmsFaceAmyotrophic Lateral SclerosisDimethylpolysiloxanesElastic ModulusEquipment DesignHumansModels, BiologicalMonitoring, PhysiologicReproducibility of ResultsSkinSmilingbaysilonDimethylpolysiloxanes

Identifiers

What OpenQuestion holds

Textmetadata
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.