Sampling Rate Prediction of Biosensors in Wireless Body Area Networks using Deep-Learning Methods

Mehrani, Mohammad; Attarzadeh, Iman; Hosseinzadeh, Mehdi

doi:10.1016/j.simpat.2020.102101

Cited by 27 publications

(12 citation statements)

References 15 publications

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“…Continuous operation of all sensors and high energy consumption [27][28][29][30] Adaptive and predicting sampling rate Reducing energy consumption and traffic Lack of a method to determine risk of patient, monitoring condition by pivot just one features…”

Section: Reducing Energy Consumption and Trafficmentioning

confidence: 99%

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

et al. 2022

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Considering the importance of biosensors on the Internet of the patient body that collect vital signs and transmit them to the coordinator, energy consumption and network lifetime are essential challenges in these networks. This paper, it has been tried to present a method based on adapting sampling rate through patient's risk and discovered pattern by employing an intelligence method based on adaptive neurofuzzy inference system, interpolation function, and a biosensor patron. It causes restricting sensed and transmitted data to the coordinator. In the proposed schema, three methods containing Grid partitioning, Subtractive Clustering and fuzzy c-means have been used in two modes, including hybrid and error backpropagation, to predict the individual's behavioral pattern and determine the patient's risk, attentively. The simulation results in MATLAB R2018b show that the proposed method reduces the network communications. It has improved energy consumption by up to three times and also reduced traffic by more than 80% compared to similar methods.

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Section: Reducing Energy Consumption and Trafficmentioning

confidence: 99%

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

et al. 2022

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“…[ 142 ] In addition, it has also been used to estimate the effects of employing LSTM to predict the sampling rate of active biosensors. [ 143 ]…”

Section: Machine Learning For Biomedical Signal Processingmentioning

confidence: 99%

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Zhang

Wang

Liu

et al. 2021

Adv Healthcare Materials

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The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health‐related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning‐reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning‐reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning‐reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real‐time monitoring, out‐of‐clinic diagnosis, and onsite food safety detection are proposed.

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“…Besides the aforementioned researches, machine learning techniques for sampling rate prediction have been suggested.. [27][28][29] In Reference 27, the authors have proposed a scheme for data sampling rate prediction in biosensors of WBSNs. The network execution time is partitioned into two portions: sampling rate determination and forecasting the sampling rate.…”

Section: Related Workmentioning

confidence: 99%

Energy‐Saving Multisensor Data Sampling and Fusion with Decision‐Making for Monitoring Health Risk Using WBSNs

Jaber

Idrees

2020

Softw Pract Exp

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The necessity of developing sufficient systems to monitor health conditions has increased due to the aging of the population and the prevalence of chronic diseases, creating a demand for remote health care systems that make use of biosensors. This article proposes an energy‐saving multisensor data sampling and fusion with decision‐making for the monitoring of patient health risk in wireless body sensor networks (WBSNs). The work consists of three steps: energy‐efficient sampling rate adaptation, multisensor data fusion, and decision‐making. The sampling is performed in each biosensor and it adapts its rate based on the local risk and the global risk in which global risk computed at the coordinator, where the data is fused afterward. Finally, decisions are made according to the risk level of the patient. The processing of these functions enables in real‐time the adoption of the biosensor sampling rates based on the dynamic risk level of each biosensor, and a corresponding decision is made whenever an emergency is detected. The performance of the suggested approach is evaluated using actual health datasets, and some of its aspects are put into comparison with an existing approach, such as the data reducing and energy‐consuming rates. The acquired results illustrate a decrease in the volume of gathered data, thus a significant energy saving has been made while preserving data accuracy and integrity. Moreover, presenting a data fusing model at the coordinator level by means of an early warning score system has assessed the health condition of patients and took an appropriate decision when detecting emergencies.

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Sampling Rate Prediction of Biosensors in Wireless Body Area Networks using Deep-Learning Methods

Cited by 27 publications

References 15 publications

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

A flexible energy management approach for smart healthcare on the internet of patients (IoP)

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Energy‐Saving Multisensor Data Sampling and Fusion with Decision‐Making for Monitoring Health Risk Using WBSNs

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