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A deep-learning E-skin decodes complex human motion:
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.
[...] 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.
Journal Reference:
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
The approach could ease VR/AR implementations.
(Score: 4, Informative) by Immerman on Friday June 19 2020, @06:38PM
The demo video shows detection of which singe finger is being bent to the palm - but fails to detect the simultaneous motion of the adjacent fingers.
Clever idea, maybe even useful, but it seems extremely unlikely that it can meaningfully decode *complex* hand motion. At least in its current state, here's hoping it improves dramatically.