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Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters
Author(s) -
Huang Qingyun,
Wu Cong,
Hou Senlin,
Yao Kuanming,
Sun Hui,
Wang Yufan,
Chen Yikai,
Law Junhui,
Yang Mingxiao,
Chan Hoyin,
Roy Vellaisamy A. L.,
Zhao Yuliang,
Wang Dong,
Song Enming,
Yu Xinge,
Lao Lixing,
Sun Yu,
Li Wen Jung
Publication year - 2022
Publication title -
advanced healthcare materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.288
H-Index - 90
eISSN - 2192-2659
pISSN - 2192-2640
DOI - 10.1002/adhm.202201404
Subject(s) - auricle , conformable matrix , electrophysiology , computer science , biomedical engineering , inner ear , materials science , anatomy , medicine , biology , neuroscience , composite material
Underneath the ear skin there are rich vascular network and sensory nerve branches. Hence, the 3D mapping of auricular electrophysiological signals can provide new biomedical perspectives. However, it is still extremely challenging for current sensing techniques to cover the entire ultra‐curved auricle. Here, a 3D graphene‐based ear‐conformable sensing device with embedded and distributed 3D electrodes for full‐auricle physiological monitoring is reported. As a proof‐of‐concept, spatiotemporal auricular electrical skin resistance (AESR) mapping is demonstrated for the first time, and human subject‐specific AESR distributions are observed. From the data of more than 30 ears (both right and left ears), the auricular region‐specific AESR changes after cycling exercise are observed in 98% of the tests and are clustered into four groups via machine learning‐based data analyses. Correlations of AESR with heart rate and blood pressure are also studied. This 3D electronic platform and AESR‐based biometrical findings show promising biomedical applications.