A bimodal soft electronic skin for tactile and touchless interaction in real time
By Jin Ge, Xu Wang, M. Drack, O. Volkov, Mo Liang, Gilbert Santiago Cañón Bermúdez, R. Illing, Changan Wang, Shengqiang Zhou, J. Fassbender and 2 more. Nature Communications, 2019
297 citations recorded by the source corpus.
The emergence of smart electronics, human friendly robotics and supplemented or virtual reality demands electronic skins with both tactile and touchless perceptions for the manipulation of real and virtual objects. Here, we realize bifunctional electronic skins equipped with a compliant magnetic microelectromechanical system able to transduce both tactile—via mechanical pressure—and touchless—via magnetic fields—stimulations simultaneously. The magnetic microelectromechanical system separates electric signals from tactile and touchless interactions into two different regions, allowing the electronic skins to unambiguously distinguish the two modes in real time. Besides, its inherent magnetic specificity overcomes the interference from non-relevant objects and enables signal-programmable interactions. Ultimately, the magnetic microelectromechanical system enables complex interplay with physical objects enhanced with virtual content data in augmented reality, robotics, and medical applications. To realize electronic skins for emerging technologies that require multifunctional sensing capability, intelligent design strategies are needed. Here, the authors report electronic skins with a single sensory unit that simultaneously transduces both tactile and touchless stimulations.
Subfields: Biomedical Engineering, Cognitive Neuroscience, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/8bdc95c822becf8168e0bbb7c2ac14360c931e75
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In Biomedical Engineering
In Cognitive Neuroscience
Citations in the corpus
- Soft magnetic skin for super-resolution tactile sensing with force self-decoupling (2021, background): “…principles—in-cluding capacitance ( 8 – 12 ), piezoresistance ( 13 – 19 ), optics ( 20 – 22 ), magnetics ( 23 – 25 ), giant magnetoresistance ( 26 , 27 ), and barometric pressure ( 28 ) (see Table 1)—within which machine learning technologies have played an important role to map the raw sensory…”