Conductive block copolymer elastomers and psychophysical thresholding for accurate haptic effects.


Journal

Science robotics
ISSN: 2470-9476
Titre abrégé: Sci Robot
Pays: United States
ID NLM: 101733136

Informations de publication

Date de publication:
12 Jun 2024
Historique:
medline: 12 6 2024
pubmed: 12 6 2024
entrez: 12 6 2024
Statut: ppublish

Résumé

Electrotactile stimulus is a form of sensory substitution in which an electrical signal is perceived as a mechanical sensation. The electrotactile effect could, in principle, recapitulate a range of tactile experience by selective activation of nerve endings. However, the method has been plagued by inconsistency, galvanic reactions, pain and desensitization, and unwanted stimulation of nontactile nerves. Here, we describe how a soft conductive block copolymer, a stretchable layout, and concentric electrodes, along with psychophysical thresholding, can circumvent these shortcomings. These purpose-designed materials, device layouts, and calibration techniques make it possible to generate accurate and reproducible sensations across a cohort of 10 human participants and to do so at ultralow currents (≥6 microamperes) without pain or desensitization. This material, form factor, and psychophysical approach could be useful for haptic devices and as a tool for activation of the peripheral nervous system.

Identifiants

pubmed: 38865475
doi: 10.1126/scirobotics.adk3925
doi:

Substances chimiques

Elastomers 0
Polymers 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

eadk3925

Auteurs

Rachel Blau (R)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Abdulhameed Abdal (A)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.
Department of Mechanical and Aerospace Engineering, University of California, San Diego, La Jolla, CA, USA.

Nicholas Root (N)

Brain and Cognition, Psychology Department, University of Amsterdam, Amsterdam, Netherlands.

Alexander X Chen (AX)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Tarek Rafeedi (T)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Robert Ramji (R)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Yi Qie (Y)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Taewoo Kim (T)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Anthony Navarro (A)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Jason Chin (J)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Laura L Becerra (LL)

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA.

Samuel J Edmunds (SJ)

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA.

Samantha M Russman (SM)

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA.

Shadi A Dayeh (SA)

Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA.

David P Fenning (DP)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

Romke Rouw (R)

Brain and Cognition, Psychology Department, University of Amsterdam, Amsterdam, Netherlands.

Darren J Lipomi (DJ)

Aiiso Yufeng Li Family Department of Chemical and Nano Engineering, University of California, San Diego, La Jolla, CA, USA.

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Classifications MeSH