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A Wearable, Low-cost Hand Tremor Sensor for Detecting Hypoglycemic Events in Diabetic Patients

  • Hasan Abbas
  • , Karim Zahed
  • , Lejla Alic
  • , Yibo Zhu
  • , Farzan Sasangohar
  • , Ranjana Mehta
  • , Mark Lawley
  • , Qammer H. Abbasi
  • , Khalid A. Qaraqe
  • Texas A and M University at Qatar Education City
  • Texas A&M University
  • University of Glasgow

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Severe hypoglycemia leverages complication in diabetes patients: e.g., it increases death rate by a six-fold. Therefore, early detection and prediction of hypoglycemic events are of utmost importance. This publication presents a prototype of a wearable hand-tremor system that detects the onset of hypoglycemic events. The results show the prototype is capable of simulating anticipated frequency and amplitude of the tremor relevant for hypoglycemic events. The initial functional performance-tests demonstrate a maximum error of 4.75% in the detecting the tremor frequency.

Original languageEnglish
Title of host publicationRFM 2018 - 2018 IEEE International RF and Microwave Conference, Proceedings
EditorsIdnin Pasya, Aziati Husna Awang, Fauziahanim Che Seman
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages182-184
Number of pages3
ISBN (Electronic)9781538667200
DOIs
StatePublished - Dec 2018
Externally publishedYes
Event2018 IEEE International RF and Microwave Conference, RFM 2018 - Penang, Malaysia
Duration: 17 Dec 201819 Dec 2018

Publication series

NameRFM 2018 - 2018 IEEE International RF and Microwave Conference, Proceedings

Conference

Conference2018 IEEE International RF and Microwave Conference, RFM 2018
Country/TerritoryMalaysia
CityPenang
Period17/12/1819/12/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Diabetes
  • Hand tremor
  • Hypoglycemia
  • Internet of Things
  • Wearable sensor

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