An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models

The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a u...

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Published inSensors (Basel, Switzerland) Vol. 24; no. 11; p. 3429
Main Authors Mishra, Amitabh, Liberman, Lucas S., Brahamanpally, Nagaraju
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 26.05.2024
MDPI
Subjects
Algorithms
Analysis
Architecture
arterial pressure (ART)
Communication
Cost reduction
Electric power production
Electric Power Supplies
electrocardiogram (ECG/EKG)
Geriatrics
Health care expenditures
Humans
Internet
Internet of Medical Things (IoMT)
Internet of Things
Monitoring, Physiologic - methods
Patients
Physiology
Power
regression
Regression Analysis
Sensors
Smartphones
Telemedicine
electrocardiogram (ECG/EKG)
arterial pressure (ART)
Internet of Medical Things (IoMT)
regression
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Abstract The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.
AbstractList The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.
The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for continuous power needs. Additionally, there are mechanisms for better utilization of battery power for network longevity. IoMT networks pose a unique challenge with respect to sensor power replenishment as the sensors could be embedded inside the subject. A possible solution could be to reduce the amount of sensor data transmission and recreate the signal at the receiving end. This article builds upon previous physiological monitoring studies by applying new decision tree-based regression models to calculate the accuracy of reproducing data from two sets of physiological signals transmitted over cellular networks. These regression analyses are then executed over three different iteration varieties to assess the effect that the number of decision trees has on the efficiency of the regression model in question. The results indicate much lower errors as compared to other approaches indicating significant saving on the battery power and improvement in network longevity.
Audience Academic
Author Brahamanpally, Nagaraju
Mishra, Amitabh
Liberman, Lucas S.
AuthorAffiliation 1 Department of Cybersecurity and Information Technology, Hall Marcus College of Science and Engineering, University of West Florida, Pensacola, FL 32514, USA
2 Department of Computer Science, Hall Marcus College of Science and Engineering, University of West Florida, Pensacola, FL 32514, USA
AuthorAffiliation_xml – name: 2 Department of Computer Science, Hall Marcus College of Science and Engineering, University of West Florida, Pensacola, FL 32514, USA
– name: 1 Department of Cybersecurity and Information Technology, Hall Marcus College of Science and Engineering, University of West Florida, Pensacola, FL 32514, USA
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CitedBy_id crossref_primary_10_1080_09720510_2018_1471266
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Keywords electrocardiogram (ECG/EKG)
arterial pressure (ART)
Internet of Medical Things (IoMT)
regression
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Snippet The sensors used in the Internet of Medical Things (IoMT) network run on batteries and need to be replaced, replenished or should use energy harvesting for...
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SubjectTerms Algorithms
Analysis
Architecture
arterial pressure (ART)
Communication
Cost reduction
Electric power production
Electric Power Supplies
electrocardiogram (ECG/EKG)
Geriatrics
Health care expenditures
Humans
Internet
Internet of Medical Things (IoMT)
Internet of Things
Monitoring, Physiologic - methods
Patients
Physiology
Power
regression
Regression Analysis
Sensors
Smartphones
Telemedicine
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Title An Optimal, Power Efficient, Internet of Medical Things Framework for Monitoring of Physiological Data Using Regression Models
URI https://www.ncbi.nlm.nih.gov/pubmed/38894222
https://www.proquest.com/docview/3067439084
https://www.proquest.com/docview/3070802043
https://pubmed.ncbi.nlm.nih.gov/PMC11174858
https://doaj.org/article/cdb43e0a390c49b3961b96279f73059b
Volume 24
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