AI/ML-Enabled 2-D - RuS2 Nanomaterial-Based Multifunctional, Low Cost, Wearable Sensor Platform for Non-Invasive Point of Care Diagnostics

Veeralingam, Sushmitha and Khandelwal, Shivam and Badhulika, Sushmee (2020) AI/ML-Enabled 2-D - RuS2 Nanomaterial-Based Multifunctional, Low Cost, Wearable Sensor Platform for Non-Invasive Point of Care Diagnostics. IEEE Sensors Journal, 20 (15). pp. 8437-8444. ISSN 1530-437X

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Abstract

We report the first of its kind artificial intelligence/machine learning (AI/ML) enabled nanomaterial based multifunctional sensing platform for simultaneous and continuous monitoring of certain vital body parameters viz. the hydration levels of the skin, glucose concentration and pH levels in biofluid sweat with high accuracy and speed. RuS2 nanoparticles were synthesized using a facile hydrothermal method and detailed characterization revealed cubic crystal structure of laurite-RuS2 with mesoporous morphology that provided enhanced electrocatalytic sites for sensing. The biochemical sensor was fabricated using layer-by-layer spin-coating technique of RuS2 on an interdigitated PDMS substrate. Further, to facilitate human-machine interface that can analyze data from large sample sizes ( 50 sensors), the sensor was interfaced with the open-source microcontroller board (QueSSence) wherein Artificial Intelligence (AI) based K-Nearest Neighbors (KNN) algorithm enabled precise and faster data acquisition from complex mathematical conjunctures. The response of the sensor towards hydration levels for various skin conditions, glucose concentration, and pH of sweat were examined for both artificial skin and human skin. The RuS2 based sweat-glucose sensor exhibited a sensitivity of 87.9 ± 0.6 μM-1 cm-2 in a physiologically relevant range of 10 nM - 0.1 mM and limit of detection of 4.87 nM. The pH sensor exhibited a sensitivity of 71.2 ± 0.5 pH-1cm-2 across the pH range of 4 -8.5. The multifunctional sensor displayed high stability and reusability at room temperature. The optimized response was integrated with a smartphone via a customized application enabling user-friendly, real-time monitoring of the health conditions.

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IITH Creators:

IITH Creators	ORCiD
Veeralingam, Sushmitha	UNSPECIFIED
Khandelwal, Shivam	UNSPECIFIED
Badhulika, Sushmee	UNSPECIFIED

Item Type:

Article

Uncontrolled Keywords:

Artificial intelligence; Artificial organs; Body fluids; Costs; Crystal structure; Data acquisition; Glucose; Glucose sensors; Hydration; Morphology; Nearest neighbor search; pH; pH sensors; Reusability; Ruthenium compounds; Synthesis (chemical); Wearable computers;Continuous monitoring; Cubic crystal structures; Glucose concentration; Human Machine Interface; K nearest neighbor (KNN); Microcontroller boards; Multifunctional sensors; Point of care diagnostic

Subjects:

Electrical Engineering

Divisions:

Department of Electrical Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

05 Aug 2021 05:55

Last Modified:

05 Aug 2021 05:55

URI:

http://raiithold.iith.ac.in/id/eprint/8680