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Supporting Information for "Large-scale Surface Shape Sensing with  Learning-based Computational Mechanics"             
  • +7
  • Kui Wang,
  • Chi Hin Mak,
  • Justin Di-Lang Ho,
  • Zhi-Yu Liu,
  • Kam Yim Sze,
  • Kenneth K.Y. Wong,
  • Kaspar Althoefer,
  • Yunhui Liu,
  • Toshiko Fukuda,
  • Ka-Wai Kwok
Kui Wang

Corresponding Author:[email protected]

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Chi Hin Mak
Author Profile
Justin Di-Lang Ho
Zhi-Yu Liu
Kam Yim Sze
Kenneth K.Y. Wong
Kaspar Althoefer
Yunhui Liu
Toshiko Fukuda
Ka-Wai Kwok
Author Profile


    Proprioception, the ability to perceive one’s own configuration and movement in space, enables organisms to safely and accurately interact with their environment and each other. The underlying sensory nerves that make this possible are highly dense and use sophisticated communication pathways to propagate signals from nerves in muscle, skin and joints to the central nervous system wherein the organism can process and react to stimuli. In a step forward to realize robots with such perceptive capability, we propose a flexible sensor framework that incorporates a novel hybrid modeling strategy, taking advantage of computational mechanics and machine learning. We implement the sensor framework on a large, thin and flexible sensor that transforms sparsely distributed strains into continuous surface shape. Finite element (FE) analysis is utilized to determine sensor design parameters, while an FE model is built to enrich the morphological data used in the supervised training to achieve continuous surface reconstruction. A mapping between the local strain data and the enriched surface data is subsequently trained using ensemble learning. This hybrid approach enables real-time, robust and high-order surface shape reconstruction. The sensing performance is evaluated in terms of accuracy, repeatability, and feasibility with numerous scenarios, which has not been demonstrated and reported on such a large-scale (A4-paper-size) sensor before.
31 May 2021Submitted to AISY Supporting Information
30 Jun 2021Published in AISY Supporting Information
Nov 2021Published in Advanced Intelligent Systems volume 3 issue 11 on pages 2100089. 10.1002/aisy.202100089