AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients

Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citize...
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Autor*in:	Shakeel Ahmed [verfasserIn] Parvathaneni Naga Srinivasu [verfasserIn] Abdulaziz Alhumam [verfasserIn] Mohammed Alarfaj [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	AAL IoMT Type-2 diabetes biosensors DenseNet-169 hyperparameters

Übergeordnetes Werk:	In: Diagnostics - MDPI AG, 2012, 12(2022), 11, p 2739
Übergeordnetes Werk:	volume:12 ; year:2022 ; number:11, p 2739

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/diagnostics12112739

Katalog-ID:	DOAJ083819584

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language	English
source	In Diagnostics 12(2022), 11, p 2739 volume:12 year:2022 number:11, p 2739
sourceStr	In Diagnostics 12(2022), 11, p 2739 volume:12 year:2022 number:11, p 2739
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topic_facet	AAL IoMT Type-2 diabetes biosensors DenseNet-169 hyperparameters Medicine (General)
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container_title	Diagnostics
authorswithroles_txt_mv	Shakeel Ahmed @@aut@@ Parvathaneni Naga Srinivasu @@aut@@ Abdulaziz Alhumam @@aut@@ Mohammed Alarfaj @@aut@@
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callnumber-first	R - Medicine
author	Shakeel Ahmed
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topic	misc R5-920 misc AAL misc IoMT misc Type-2 diabetes misc biosensors misc DenseNet-169 misc hyperparameters misc Medicine (General)
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title	AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients
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title_auth	AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients
abstract	Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes. A glucose oxide sensor is used to monitor diabetic elderly people continuously. Spectrogram images are created from the recorded data from the sensor to assess and detect aberrant glucose levels. DenseNet-169 examines and analyzes the spectrogram pictures, and messages are sent to caregivers when aberrant glucose levels are detected. The current work describes both the spectrogram image analysis and the signal-to-spectrogram generating method. The study presents a future perspective model for a mobile application for real-time patient monitoring. Benchmark metrics evaluate the application’s performances, including sensitivity, specificity, accuracy, and F1-score. Several cross--validations are used to evaluate the model’s performance. The findings demonstrate that the proposed model can correctly identify patients with abnormal blood glucose levels.
abstractGer	Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes. A glucose oxide sensor is used to monitor diabetic elderly people continuously. Spectrogram images are created from the recorded data from the sensor to assess and detect aberrant glucose levels. DenseNet-169 examines and analyzes the spectrogram pictures, and messages are sent to caregivers when aberrant glucose levels are detected. The current work describes both the spectrogram image analysis and the signal-to-spectrogram generating method. The study presents a future perspective model for a mobile application for real-time patient monitoring. Benchmark metrics evaluate the application’s performances, including sensitivity, specificity, accuracy, and F1-score. Several cross--validations are used to evaluate the model’s performance. The findings demonstrate that the proposed model can correctly identify patients with abnormal blood glucose levels.
abstract_unstemmed	Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes. A glucose oxide sensor is used to monitor diabetic elderly people continuously. Spectrogram images are created from the recorded data from the sensor to assess and detect aberrant glucose levels. DenseNet-169 examines and analyzes the spectrogram pictures, and messages are sent to caregivers when aberrant glucose levels are detected. The current work describes both the spectrogram image analysis and the signal-to-spectrogram generating method. The study presents a future perspective model for a mobile application for real-time patient monitoring. Benchmark metrics evaluate the application’s performances, including sensitivity, specificity, accuracy, and F1-score. Several cross--validations are used to evaluate the model’s performance. The findings demonstrate that the proposed model can correctly identify patients with abnormal blood glucose levels.
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title_short	AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients
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