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Towards Cognitive Authentication for Smart Healthcare Applications
Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949031/ https://www.ncbi.nlm.nih.gov/pubmed/35336276 http://dx.doi.org/10.3390/s22062101 |
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author | Sodhro, Ali Hassan Sennersten, Charlotte Ahmad, Awais |
author_facet | Sodhro, Ali Hassan Sennersten, Charlotte Ahmad, Awais |
author_sort | Sodhro, Ali Hassan |
collection | PubMed |
description | Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events. |
format | Online Article Text |
id | pubmed-8949031 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89490312022-03-26 Towards Cognitive Authentication for Smart Healthcare Applications Sodhro, Ali Hassan Sennersten, Charlotte Ahmad, Awais Sensors (Basel) Article Secure and reliable sensing plays the key role for cognitive tracking i.e., activity identification and cognitive monitoring of every individual. Over the last years there has been an increasing interest from both academia and industry in cognitive authentication also known as biometric recognition. These are an effect of individuals’ biological and physiological traits. Among various traditional biometric and physiological features, we include cognitive/brainwaves via electroencephalogram (EEG) which function as a unique performance indicator due to its reliable, flexible, and unique trait resulting in why it is hard for an un-authorized entity(ies) to breach the boundaries by stealing or mimicking them. Conventional security and privacy techniques in the medical domain are not the potential candidates to simultaneously provide both security and energy efficiency. Therefore, state-of-the art biometrics methods (i.e., machine learning, deep learning, etc.) their applications with novel solutions are investigated and recommended. The experimental setup considers EEG data analysis and interpretation of BCI. The key purpose of this setup is to reduce the number of electrodes and hence the computational power of the Random Forest (RF) classifier while testing EEG data. The performance of the random forest classifier was based on EEG datasets for 20 subjects. We found that the total number of occurred events revealed 96.1% precision in terms of chosen events. MDPI 2022-03-09 /pmc/articles/PMC8949031/ /pubmed/35336276 http://dx.doi.org/10.3390/s22062101 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sodhro, Ali Hassan Sennersten, Charlotte Ahmad, Awais Towards Cognitive Authentication for Smart Healthcare Applications |
title | Towards Cognitive Authentication for Smart Healthcare Applications |
title_full | Towards Cognitive Authentication for Smart Healthcare Applications |
title_fullStr | Towards Cognitive Authentication for Smart Healthcare Applications |
title_full_unstemmed | Towards Cognitive Authentication for Smart Healthcare Applications |
title_short | Towards Cognitive Authentication for Smart Healthcare Applications |
title_sort | towards cognitive authentication for smart healthcare applications |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8949031/ https://www.ncbi.nlm.nih.gov/pubmed/35336276 http://dx.doi.org/10.3390/s22062101 |
work_keys_str_mv | AT sodhroalihassan towardscognitiveauthenticationforsmarthealthcareapplications AT sennerstencharlotte towardscognitiveauthenticationforsmarthealthcareapplications AT ahmadawais towardscognitiveauthenticationforsmarthealthcareapplications |