Biometric authentication for dementia patients with recurrent neural network

Research output: Chapter in Book / Conference PaperConference Paperpeer-review

5 Citations (Scopus)

Abstract

![CDATA[The usage of technology to monitor the health status of patients with chronic diseases continue to rise. Dementia is one such chronic disease which demands continuous monitoring and observation to keep track of the health status of the patient. The dementia patients use different services to contact healthcare workers or doctors. These services need login credentials. However, due to progressive and frequent memory loss and confusion, they face significant challenges to access the services. Hence, biometric authentication can play a crucial role to provide better support for them. This paper proposes a biometric-based authentication framework based on a recurrent neural network for dementia patients. The PPG and ECG signals from the wearable devices are examined for authentication purpose. Two distinct features of the signals: Instantaneous frequency spectral entropy are provisioned to the LSTM network to train the system. From the dataset of ten participants, the accuracy of the PPG and ECG based identifications reached to 100% and 88.9% and F1 scores reached to 1.00 and 0.86 respectively.]]
Original languageEnglish
Title of host publicationProceedings 2019 International Conference on Electrical Engineering Research & Practice (iCEERP), 24-28 November 2019, Western Sydney University, Parramatta Campus, Sydney, Australia
PublisherIEEE
Pages7-12
Number of pages6
ISBN (Print)9781728166575
DOIs
Publication statusPublished - 2019
EventInternational Conference on Electrical Engineering Research and Practice -
Duration: 24 Nov 2019 → …

Conference

ConferenceInternational Conference on Electrical Engineering Research and Practice
Period24/11/19 → …

Keywords

  • biometric identification
  • dementia
  • patients
  • wearable technology

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