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

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Yang, H et al 2022 (3.08 MB)
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Date

2022-09-09

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Citation

Yang, H., Li, J., Xiao, X. et al. Topographic design in wearable MXene sensors with in-sensor machine learning for full-body avatar reconstruction. Nat Commun 13, 5311 (2022).

Abstract

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 insensor 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.

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Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.

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