Enabling Technologies on Cloud of Things for Smart Healthcare

Mahmoud, Mukhtar M. E.; Rodrigues, Joel J. P. C.; Ahmed, Syed Hassan; Shah, Sayed Chhattan; Al‐Muhtadi, Jalal; Korotaev, Valery V.; Albuquerque, Victor Hugo C. de

doi:10.1109/access.2018.2845399

Cited by 102 publications

(33 citation statements)

References 89 publications

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“…Similar form of discussion has been carried out by Meharouech et al [14]. Mahmoud et al [15] have performed an investigation towards cloud-of-things architecture associated with the healthcare system. The authors have mainly emphasized over energy efficiency factor in this presented system.…”

Section: Introductionmentioning

confidence: 84%

Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics

Akanksha

2020

IJECE

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Abnormal level of stress is the root indicator factor to have significant impact over the health of heart and there is a close relationship between the stress levels with heart rate. Review of the existing literature showcase that there has been various work that has been carried out towards investigation of considering heart rate with an internet-of-things (IoT) system. Apart from this, existing system doesnt offer any instantaneous solution where certain intimation is offered in real-time to the user with wearables as a solution to control the stress condition. Therefore, the current paper introduces a novel framework where the sampled heart rates of the patients are captured by IoT deivices. The aggregated data are further forwarded to the cloud analytic system that uses correlation to extract the appropriate message. The system after being applied with teh machine learning approach could further extract the elite outcome followed by forwarding the contextual data to teh user. Using an analytical modelliig, the proposed system shows that it offers better accuracy and reduced processing time when compared with other machine learning approach and thereby it proves to be cost effective solution in IoT system over medical case study.

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Section: Introductionmentioning

confidence: 84%

Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics

Akanksha

2020

IJECE

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“…[12] CoT: Things (devices/sensors) of IoT when combines with the power of cloud, the new platform thus is termed as Cloud of Things (CoT). [13] In CoT cloud is in the middle of the architecture used as an interface between the different IoT devices communication each other. Cloud platform is also used to store and process the data generated via sensors used in IoT.…”

Section: Preliminariesmentioning

confidence: 99%

A Blockchain and FOT (Fog of Things) based system and method for predicting an Infectious disease transmitted by a virulent respiratory virus

Tewari

Kumar

2020

Preprint

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The present research is generally related to a system and method for predicting an infectious disease such as COVID-19 transmitted by a virulent respiratory virus. Disclosed are a system and method for predicting an infectious disease transmitted by a virulent respiratory virus. The system includes a plurality of Internet of Things (IoT) sensors, a plurality of fog node devices, and a plurality of computing devices in cloud data centers. The IoT sensors are configured to be coupled to a plurality of human beings to generate a health dataset. The fog node devices are associated with a fog layer to receive the health dataset from the IoT sensors to process and to store the health dataset over a blockchain network. The fog node devices process the health dataset at the fog layer by performing a fog computing. The computing devices and cloud data centers receive the processed health dataset from the of fog node devices over the blockchain network. This research is also filed for patent in Indian Patent Office with application number – 202011021969.

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“…Authors have identified challenges in the design of IoHT system and provided a system for future trends. Mukhtar et al [39] analyzed the cloud of Things (CoT) architectures from the perspective of its usage for efficient and smart healthcare.…”

Section: State Of the Artmentioning

confidence: 99%

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

Riaz¹,

Azad

Arshad

et al. 2019

Future Generation Computer Systems

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Preventive healthcare requires continuous monitoring of the blood pressure (BP) of patients, which is not feasible using conventional methods. Photoplethysmogram (PPG) signals can be effectively used for this purpose as there is a physiological relation between the pulse width and BP and can be easily acquired using a wearable PPG sensor. However, developing real-time algorithms for wearable technology is a significant challenge due to various conflicting requirements such as high accuracy, computationally constrained devices, and limited power supply. In this paper, we propose a novel feature set for continuous, real-time identification of abnormal BP. This feature set is obtained by identifying the peaks and valleys in a PPG signal (using a peak detection algorithm), followed by the calculation of rising time, falling time and peak-to-peak distance. The histograms of these times are calculated to form a feature set that can be used for classification of PPG signals into one of the two classes: normal or abnormal BP. No public dataset is available for such study and therefore a prototype is developed to collect PPG signals alongside BP measurements. The proposed feature set shows very good performance with an overall accuracy of approximately 95%. Although the proposed feature set is effective, the significance of individual features varies greatly (validated using significance testing) which led us to perform weighted voting of features for classification by performing autoregressive modeling. Our experiments show that the simplest linear classifiers produce very good results indicating the strength of the proposed feature set. The weighted voting improves the results significantly, producing an overall accuracy of about 98%. Conclusively, the PPG signals can be effectively used to identify BP, and the proposed feature set is efficient and computationally feasible for implementation on standalone devices.

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Enabling Technologies on Cloud of Things for Smart Healthcare

Cited by 102 publications

References 89 publications

Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics

Framework for propagating stress control message using heartbeat based IoT remote monitoring analytics

A Blockchain and FOT (Fog of Things) based system and method for predicting an Infectious disease transmitted by a virulent respiratory virus

Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor

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