A deep-learning E-skin decodes complex human motion

Date:
June 18, 2020
Source:
The Korea Advanced Institute of Science and Technology (KAIST)
Summary:
A deep-learning powered single-strained electronic skin sensor can
capture human motion from a distance. The single strain sensor
placed on the wrist decodes complex five-finger motions in real
time with a virtual 3D hand that mirrors the original motions. The
deep neural network boosted by rapid situation learning (RSL)
ensures stable operation regardless of its position on the surface
of the skin.



FULL STORY ==========================================================================
A deep-learning powered single-strained electronic skin sensor can capture human motion from a distance. The single strain sensor placed on the
wrist decodes complex five-finger motions in real time with a virtual 3D
hand that mirrors the original motions. The deep neural network boosted
by rapid situation learning (RSL) ensures stable operation regardless
of its position on the surface of the skin.


========================================================================== Conventional approaches require many sensor networks that cover the
entire curvilinear surfaces of the target area. Unlike conventional
wafer-based fabrication, this laser fabrication provides a new sensing
paradigm for motion tracking.

The research team, led by Professor Sungho Jo from the School of
Computing, collaborated with Professor Seunghwan Ko from Seoul National University to design this new measuring system that extracts signals corresponding to multiple finger motions by generating cracks in metal nanoparticle films using laser technology. The sensor patch was then
attached to a user's wrist to detect the movement of the fingers.

The concept of this research started from the idea that pinpointing
a single area would be more efficient for identifying movements than
affixing sensors to every joint and muscle. To make this targeting
strategy work, it needs to accurately capture the signals from different
areas at the point where they all converge, and then decoupling the
information entangled in the converged signals. To maximize users'
usability and mobility, the research team used a single-channeled sensor
to generate the signals corresponding to complex hand motions.

The rapid situation learning (RSL) system collects data from arbitrary
parts on the wrist and automatically trains the model in a real-time demonstration with a virtual 3D hand that mirrors the original motions. To enhance the sensitivity of the sensor, researchers used laser-induced
nanoscale cracking.

This sensory system can track the motion of the entire body with a small sensory network and facilitate the indirect remote measurement of human motions, which is applicable for wearable VR/AR systems.

The research team said they focused on two tasks while developing
the sensor.

First, they analyzed the sensor signal patterns into a latent space encapsulating temporal sensor behavior and then they mapped the latent
vectors to finger motion metric spaces.

Professor Jo said, "Our system is expandable to other body parts. We
already confirmed that the sensor is also capable of extracting gait
motions from a pelvis. This technology is expected to provide a turning
point in health- monitoring, motion tracking, and soft robotics."

========================================================================== Story Source: Materials provided by The_Korea_Advanced_Institute_of_Science_and_Technology_ (KAIST). Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Kim, K. K., et al. A deep-learned skin sensor decoding the
epicentral
human motions. Nature Communications, 2020 DOI:
10.1038/s41467-020-16040- y29 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/06/200618094617.htm

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