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

Yang, Haitao1; Li, Jiali1; Xiao, Xiao2; Wang, Jiahao3; Li, Yufei1; Li, Kerui1; Li, Zhipeng3; Yang, Haochen4; Wang, Qian1; Yang, Jie1; Ho, John S.3; Yeh, Po-Len5; Mouthaan, Koen3; Wang, Xiaonan6; Shah, Sahil7; Chen, Po-Yen4,8

2022-09-09

10.1038/s41467-022-33021-5

NATURE COMMUNICATIONS   Impact Factor & Quartile

2041-1723

["Wearable sensors with edge computing are desired for human motion monitoring. Here, the authors demonstrate a topographic design for wearable MXene sensor modules with wireless streaming or in-sensor computing models for avatar reconstruction.","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 Ti3C2Tx 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."]

SCI

English

Others

Start-Up Fund of University of Maryland, College Park[2957431] ; MOST-AFOSR Taiwan Topological and Nanostructured Materials Grant["FA2386-21-1-4065","5284212"] ; Maryland Energy Innovation Institute (MEI2) Energy Seed Grant[2957597]

Science & Technology - Other Topics

Multidisciplinary Sciences

WOS:000853200800022

NATURE PORTFOLIO

Web of Science

1.Natl Univ Singapore, Dept Chem & Biomol Engn, 4 Engn Dr 4, Singapore 117585, Singapore
2.Southern Univ Sci & Technol, Dept Elect & Elect Engn, Shenzhen, Peoples R China
3.Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117583, Singapore
4.Univ Maryland, Dept Chem & Biomol Engn, College Pk, MD 20740 USA
5.Realtek, Singapore 609930, Singapore
6.Tsinghua Univ, Dept Chem Engn, Beijing 100084, Peoples R China
7.Univ Maryland, Dept Elect & Comp Engn, College Pk, MD 20740 USA
8.Maryland Robot Ctr, College Pk, MD 20740 USA

Yang, Haitao,Li, Jiali,Xiao, Xiao,et al. Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction[J]. NATURE COMMUNICATIONS,2022,13(1).

Yang, Haitao.,Li, Jiali.,Xiao, Xiao.,Wang, Jiahao.,Li, Yufei.,...&Chen, Po-Yen.(2022).Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction.NATURE COMMUNICATIONS,13(1).

Yang, Haitao,et al."Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction".NATURE COMMUNICATIONS 13.1(2022).