Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction.

Autor: Yang H; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore., Li J; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore., Xiao X; Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China., Wang J; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore., Li Y; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore., Li K; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore., Li Z; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore., Yang H; Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20740, USA., Wang Q; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore., Yang J; Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore, 117585, Singapore., Ho JS; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore., Yeh PL; Realtek, Singapore, 609930, Singapore., Mouthaan K; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117583, Singapore., Wang X; Department of Chemical Engineering, Tsinghua University, 100084, Beijing, China., Shah S; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, 20740, USA. sshah389@umd.edu., Chen PY; Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, 20740, USA. checp@umd.edu.; Maryland Robotics Center, College Park, MD, 20740, USA. checp@umd.edu.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2022 Sep 09; Vol. 13 (1), pp. 5311. Date of Electronic Publication: 2022 Sep 09.
DOI: 10.1038/s41467-022-33021-5
Abstrakt: Wearable strain sensors that detect joint/muscle strain changes become prevalent at human-machine interfaces for full-body motion monitoring. However, most wearable devices cannot offer customizable opportunities to match the sensor characteristics with specific deformation ranges of joints/muscles, resulting in suboptimal performance. Adequate wearable strain sensor design is highly required to achieve user-designated working windows without sacrificing high sensitivity, accompanied with real-time data processing. Herein, wearable Ti 3 C 2 T x MXene sensor modules are fabricated with in-sensor machine learning (ML) models, either functioning via wireless streaming or edge computing, for full-body motion classifications and avatar reconstruction. Through topographic design on piezoresistive nanolayers, the wearable strain sensor modules exhibited ultrahigh sensitivities within the working windows that meet all joint deformation ranges. By integrating the wearable sensors with a ML chip, an edge sensor module is fabricated, enabling in-sensor reconstruction of high-precision avatar animations that mimic continuous full-body motions with an average avatar determination error of 3.5 cm, without additional computing devices.
(© 2022. The Author(s).)
Databáze: MEDLINE
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