A deep-learned skin sensor decoding the epicentral human motions

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State monitoring of the complex system needs a large number of sensors. Especially, studies in soft electronics aim to attain complete measurement of the body, mapping various stimulations like temperature, electrophysiological signals, and mechanical strains. However, conventional approach requires many sensor networks that cover the entire curvilinear surfaces of the target area. We introduce a new measuring system, a novel electronic skin integrated with a deep neural network that captures dynamic motions from a distance without creating a sensor network. The device detects minute deformations from the unique laser-induced crack structures. A single skin sensor decodes the complex motion of five finger motions in real-time, and the rapid situation learning (RSL) ensures stable operation regardless of its position on the wrist. The sensor is also capable of extracting gait motions from pelvis. This technology is expected to provide a turning point in health-monitoring, motion tracking, and soft robotics. 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.
Publisher
NATURE COMMUNICATIONS
Issue Date
2020-05
Language
English
Article Type
Article
Citation

NATURE COMMUNICATIONS, v.11, no.1

ISSN
2041-1723
DOI
10.1038/s41467-020-16040-y
URI
http://hdl.handle.net/10203/275512
Appears in Collection
CS-Journal Papers(저널논문)
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