Microneedle Based ECG – Glucose Painless MEMS Sensor with Analog Front End for Portable Devices - Inria - Institut national de recherche en sciences et technologies du numérique
Communication Dans Un Congrès Année : 2017

Microneedle Based ECG – Glucose Painless MEMS Sensor with Analog Front End for Portable Devices

Résumé

A portable microelectromechanical system (MEMS) for mobile phones, or other portable devices, that measures body electrical signals, as well as, extracts transdermal biological fluid for invivo analysis is proposed. This system integrates two sensing methods: three points finger electrocardiography (ECG) and glucose monitoring, through one electrode with a microneedle-array. This work presents the: (1) device modeling and microneedle-array’ fabrication method, (2) signal processing and biasing circuitry’ design and simulation, (3) Analog Front End (AFE) for measured signals, and (4) Glucose sensor characterization. Design parameters and geometries are obtained by solving the capillarity model inside the microneedles and running optimization numeric methods. The AFE consists in a differential band pass filter that provides amplification, filtering, and noise rejection. This work presents clear technological innovation, for its miniaturization and integration of known biological signals’ measurement methods in a portable Smart System, which points in the direction of Internet of Things’ goals.
Fichier principal
Vignette du fichier
448071_1_En_45_Chapter.pdf (1.04 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01629596 , version 1 (06-11-2017)

Licence

Identifiants

Citer

Miguel Lima Teixeira, Camilo Velez, Dian Li, João Goes. Microneedle Based ECG – Glucose Painless MEMS Sensor with Analog Front End for Portable Devices. 8th Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS), May 2017, Costa de Caparica, Portugal. pp.463-478, ⟨10.1007/978-3-319-56077-9_45⟩. ⟨hal-01629596⟩
251 Consultations
378 Téléchargements

Altmetric

Partager

More