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

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 Ti₃C₂T_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 topogra

SUBMITTER: Yang H 

PROVIDER: S-EPMC9461448 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature