A deep-learned skin sensor decoding the epicentral human motions

Kyun Kyu Kim; InHo Ha; Min Kim; Joonhwa Choi; Phillip Won; Sungho Jo; Seung Hwan Ko

doi:10.1038/s41467-020-16040-y

Nature Communications (May 2020)

A deep-learned skin sensor decoding the epicentral human motions

Kyun Kyu Kim,
InHo Ha,
Min Kim,
Joonhwa Choi,
Phillip Won,
Sungho Jo,
Seung Hwan Ko

Affiliations

Kyun Kyu Kim: Applied Nano and Thermal Science Lab, Department of Mechanical Engineering, Seoul National University
InHo Ha: Applied Nano and Thermal Science Lab, Department of Mechanical Engineering, Seoul National University
Min Kim: Neuro-Machine Augmented Intelligence Laboratory, School of Computing, Korea Advanced Institute of Science and Technology (KAIST)
Joonhwa Choi: Applied Nano and Thermal Science Lab, Department of Mechanical Engineering, Seoul National University
Phillip Won: Applied Nano and Thermal Science Lab, Department of Mechanical Engineering, Seoul National University
Sungho Jo: Neuro-Machine Augmented Intelligence Laboratory, School of Computing, Korea Advanced Institute of Science and Technology (KAIST)
Seung Hwan Ko: Applied Nano and Thermal Science Lab, Department of Mechanical Engineering, Seoul National University

DOI: https://doi.org/10.1038/s41467-020-16040-y
Journal volume & issue: Vol. 11, no. 1
pp. 1 – 8

Abstract

Read online

Real-time monitoring human motions normally demands connecting a large number of sensors in a complicated network. To make it simpler, Kim et al. decode the motion of fingers using a flexible sensor attached on wrist that measures skin deformation with the help of a deep-learning architecture.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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