Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Pintavirooj, C.; Keatsamarn, Tanapon; Treebupachatsakul, Treesukon

doi:10.3390/healthcare9030285

Cited by 8 publications

(5 citation statements)

References 25 publications

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“…On the other hand, the WebSocket protocol allows the server to transmit responses at any moment, even if the client does not initiate a request (bidirectional) [28]. Because the data transmission process is faster than the standard HTTP protocol, WebSocket can be used to build a system that requires real-time information transmission [29]. In this study, WebSocket could continuously be used to update information on the location of the bus that is running and could also be used to obtain real-time bus arrival schedule information at a bus stop.…”

Section: B Websocketmentioning

confidence: 99%

Real-Time Bus Arrival Time Estimation API using WebSocket in Microservices Architecture

Nugraha¹

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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In almost all countries, public transportation is the primary choice for people to make daily trips. One of the main problems of public transportation is the accuracy of the vehicle arrival schedule information. In some types of public transportation with particular tracks, such as rapid transit (e.g., MRT), the arrival time can be predicted easily because traffic jams do not affect transportation. This is different from buses, which use the same route as other vehicles so that traffic jams could affect them. This study tries to solve this problem by building an API that can be used to get information on the estimated arrival time of the bus. Estimated time is calculated based on the condition of the bus position and the actual condition of highway traffic from Google Directions API. The API was built using microservices technology, so it can be done quickly if it is further developed to a larger scale. The test was conducted on the Trans Jogja Bus by taking two routes, the short and the long routes. Each route could be explored 20 times under varying time and traffic conditions. Then, the difference between the estimated bus arrival time and the actual bus arrival time could be calculated on each trip. Based on the test results, the estimated bus arrival time generated by the API can be said to be entirely accurate because the difference between the estimated time and the actual bus arrival time is less than 30 seconds.

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Section: B Websocketmentioning

confidence: 99%

Real-Time Bus Arrival Time Estimation API using WebSocket in Microservices Architecture

Nugraha¹

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…The main limitations with the RPC and RMI are live client and server connection stability, where intermittent service changes make the connection unreliable for the applications. The connection-enabled framework based on sockets is presented in [ 3 , 5 ]. The study achieved a strong connection and stored the running data on the cache without disruption of the request of applications during their executions.…”

Section: Related Workmentioning

confidence: 99%

“…The RMI, CORBA, and RPC exploit the socket-based architecture where users and servers are separated, as in the client and server architecture. The client and server architectures are widely designed based on socket programming, where socket client and socket server classes are designed into the application programming interface [ 3 , 4 , 5 ]. Recently, the socket integrated cloud computing services to enhance the performance of the client–server model to support healthcare applications.…”

Section: Introductionmentioning

confidence: 99%

“…Challenges: In practice, the existing blockchain technologies such as Ethereum, Corda, Fabric, and IBM are widely exploited for healthcare applications. However, many research challenges exist in the current blockchain technologies and their approaches to RPC socket architectures [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ] for healthcare applications. (i) The existing client–server architecture based on socket RPC suffers from high processing costs for cloud computing.…”

Section: Introductionmentioning

confidence: 99%

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Blockchain Socket Factories with RMI-Enabled Framework for Fine-Grained Healthcare Applications

Ahmed

Lakhan

Thinnukool

et al. 2022

Sensors

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The usage of digital and intelligent healthcare applications on mobile devices has grown progressively. These applications are generally distributed and access remote healthcare services on the user’s applications from different hospital sources. These applications are designed based on client–server architecture and different paradigms such as socket, remote procedure call, and remote method invocation (RMI). However, these existing paradigms do not offer a security mechanism for healthcare applications in distributed mobile-fog-cloud networks. This paper devises a blockchain-socket-RMI-based framework for fine-grained healthcare applications in the mobile-fog-cloud network. This study introduces a new open healthcare framework for applied research purposes and has blockchain-socket-RMI abstraction level classes for healthcare applications. The goal is to meet the security and deadline requirements of fine-grained healthcare tasks and minimize execution and data validation costs during processing applications in the system. This study introduces a partial proof of validation (PPoV) scheme that converts the workload into the hash and validates it among mobile, fog, and cloud nodes during offloading, execution, and storing data in the secure form. Simulation discussions illustrate that the proposed blockchain-socket-RMI minimizes the processing and blockchain costs and meets the security and deadline requirements of fine-grained healthcare tasks of applications as compared to existing frameworks in work.

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“…For this reason, there is the spread of the development of practical and reliable telemedicine solutions [1][2][3][4] to guarantee monitoring at home and at hospital with the aim of ensuring early identification and prevention of cardiovascular diseases, hypertension, and other related diseases. As concerns BP measurement, traditional cuff-based devices have several disadvantages because they are not always accurate, they need appropriate calibration, and they do not allow continuous monitoring since performing a measurement requires about one minute or more.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Blood Pressure Sensing via Machine Learning

Attivissimo,

D’Alessandro,

De Palma

et al. 2023

Sensors

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In this paper, a machine learning (ML) approach to estimate blood pressure (BP) using photoplethysmography (PPG) is presented. The final aim of this paper was to develop ML methods for estimating blood pressure (BP) in a non-invasive way that is suitable in a telemedicine health-care monitoring context. The training of regression models useful for estimating systolic blood pressure (SBP) and diastolic blood pressure (DBP) was conducted using new extracted features from PPG signals processed using the Maximal Overlap Discrete Wavelet Transform (MODWT). As a matter of fact, the interest was on the use of the most significant features obtained by the Minimum Redundancy Maximum Relevance (MRMR) selection algorithm to train eXtreme Gradient Boost (XGBoost) and Neural Network (NN) models. This aim was satisfactorily achieved by also comparing it with works in the literature; in fact, it was found that XGBoost models are more accurate than NN models in both systolic and diastolic blood pressure measurements, obtaining a Root Mean Square Error (RMSE) for SBP and DBP, respectively, of 5.67 mmHg and 3.95 mmHg. For SBP measurement, this result is an improvement compared to that reported in the literature. Furthermore, the trained XGBoost regression model fulfills the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) as well as grade A of the British Hypertension Society (BHS) standard.

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Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Cited by 8 publications

References 25 publications

Real-Time Bus Arrival Time Estimation API using WebSocket in Microservices Architecture

Real-Time Bus Arrival Time Estimation API using WebSocket in Microservices Architecture

Blockchain Socket Factories with RMI-Enabled Framework for Fine-Grained Healthcare Applications

Non-Invasive Blood Pressure Sensing via Machine Learning

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