Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System.

Autor: Cheng YJ; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA., Li T; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA., Lee C; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA., Sakthivelpathi V; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA., Hahn JO; Department of Mechanical Engineering, University of Maryland, College Park, MD, 20742, USA., Kwon Y; Division of Cardiology, University of Washington, Seattle, WA, 98195, USA., Chung JH; Department of Mechanical Engineering, University of Washington, Seattle, WA, 98195, USA.
Jazyk: angličtina
Zdroj: Small (Weinheim an der Bergstrasse, Germany) [Small] 2024 Nov; Vol. 20 (48), pp. e2405224. Date of Electronic Publication: 2024 Sep 09.
DOI: 10.1002/smll.202405224
Abstrakt: A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.
(© 2024 Wiley‐VCH GmbH.)
Databáze: MEDLINE
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