Mapping of Spatiotemporal Auricular Electrophysiological Signals Reveals Human Biometric Clusters. Issue 23 (31st October 2022)

• Record Type: Journal Article
• Authors: Huang, Qingyun; Wu, Cong; Hou, Senlin; Yao, Kuanming; Sun, Hui; Wang, Yufan; Chen, Yikai; Law, Junhui; Yang, Mingxiao; Chan, Ho‐yin; Roy, Vellaisamy A. L.; Zhao, Yuliang; Wang, Dong; Song, Enming; Yu, Xinge; Lao, Lixing; Sun, Yu; Li, Wen Jung
• Is Part Of: Advanced healthcare materials. Volume 11:Issue 23(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-31
• Subjects: full‐auricle electrophysiological monitoring -- graphene‐based 3D electrodes -- human biometric clusters -- machine learning -- personalized healthcare sensors; Biomedical materials -- Periodicals; 610.28
• DOI: 10.1002/adhm.202201404
• Languages: English
• ISSNs: 2192-2640

Abstract

Abstract: 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. Abstract : A new 3D contoured graphene‐based sensing array is used to map real‐time spatiotemporal auricular electrical skin resistance (AESR) signals. Through machine learning‐based data analysis, the human subject‐specific AESR distributions and auricular region‐specific AESR changes after cycling exercise are observed from the signals of more than 30 ears, which reveal the human biometric clusters for the first time.