Use of wireless system in healthcare for developing countries

Salam, Humaira Abdus; Khan, Bilal

doi:10.1016/j.dcan.2015.11.001

Cited by 14 publications

(7 citation statements)

References 24 publications

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“…WBAN is composed of sensors and actuators linked by a computing unit. Sensors measure biological parameters such as blood pressure, temperature, sugar, echo-cardiograph (ECG), electroencephalograph (EEG) and other body vital signs while actuators respond to the instruction based on the sensor measurement after computation [11]. For example, in diabetic illness, the actuator regulates blood sugar by controlling the insulin level.…”

Section: Wban Architecturementioning

confidence: 99%

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

2021

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This study presents a survey of the current issues, application areas, findings, and performance challenges in wireless body area networks (WBAN). The survey discusses selected areas in WBAN signal processing, network reliability, spectrum management, security, and WBAN integration with other technologies for highly efficient future healthcare applications. The foundation of the study bases on the recent growing advances in microelectronic technology and commercialization, which ease device availability, miniaturization, and communication. The survey considers a systemic review conducted using reports, standard documents, and peer-reviewed articles. Based on the comprehensive review, we find WBANs faces several operational, standardization, and security issues, affecting performance and maintenance of user safety and privacy. We envision the increasing dependency of future healthcare on WBAN for medical and non-medical applications due to internet connectivity advances. In this view, despite the WBAN advantages in remote health monitoring, further studies need to be conducted for performance optimization. Therefore we finalize our study by proposing various current and future research directions and open issues in WBAN’s performance enhancement.

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Section: Wban Architecturementioning

confidence: 99%

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

2021

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“…One of the major applications of wireless systems is healthcare that possessed vital issues. Another requirement, which is mattered for developing countries is to have a low-cost and reliable network with effective protocols [6]. Beacons are based on Bluetooth Low Energy (BLE), which is a medium ranged transmission standard targeting for inexpensive devices with long life batteries [7,8].…”

Section: Literature Reviewmentioning

confidence: 99%

A Development of LBS by Using Beacon BLE Technology in Healthcare

Mesbah¹,

E²

2021

Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 Feb

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Wireless sensor networks are becoming popular in health-care systems day by day with the rapid growth of new technologies and application, including Location-based aware systems. Following the comprehensive health-care reforms in past years, it motivates researchers to improve approachability to health-care specially in hospitals and health centers. The main purpose of this research is to develop a model for using of Location-based systems through iBeacon and Bluetooth Low Energy technologies in health-care. The proposed model is developed for hospitals as one of the active places, where time is critical. One of the major problems in the hospital environment is often the waiting time, procedures and finding appropriate departments, which are because of large population and the size of a hospital. To endeavor this dilemma, we developed a model that easily can help for the centers and places to visited by patients through their smartphone devices and examine it in a real indoor environment. The results show that, the proposed model can significantly improve hospital information management and patient guidance, and keep them up-to-date and provide detailed patient database, patient's information and location to keep them track of doctor's appointment, and receiving a medication. With using different sensors, BLS, iBeacon and BLE, we can align patient, doctor, nurse, and pharmacy vendor to the specific purpose of satisfying patient's needs.

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“…Wireless simulation of the heterogeneous network comprised of the mobile cognitive radio and physiological sensors is executed for the effective real-time monitoring of maternal health issues during the indispensable period of gestation [12]. A crosslayer protocol for heterogeneous body area networks is designed based on the source node synergetic paradigm to address the human health management issues across the developing world with the primitive resource-restrained medical amenities [11]. In addition, the body temperature and heart rate measuring sensors are employed for assessment by the health personnel by using various types of economical hardware devices such as Arduino microcontroller interface, LCD output layout, temperature and pulse sensors [3].…”

Section: Related Workmentioning

confidence: 99%

“…Next, we evaluate the accuracy performance of the proposed fuzzy-based cardiovascular patient assessment model using various error metrics. These include the normalized root-mean-square error, 6)- (11). Here, N is the total number of samples contemplated for the associated error assessment, ξ is the normalized mean of the measured data δ observed over the specified interval of sample instances, δ i is the…”

Section: Simulation Environment Settingmentioning

confidence: 99%

Multivariate Fuzzy Logic Based Smart Healthcare Monitoring for Risk Evaluation of Cardiac Patients

Mehta

2021

Studies in Computational Intelligence

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In this chapter, the multi-dimensional fuzzy logic design is implemented to develop smart and pervasive healthcare application systems for determining the risk status of cardiac patients. This heuristic decision-making and fuzzy reasoning mechanism is based on the medical observations of several miscellaneous physiological conditions of systolic blood pressure, blood sugar level, serum cholesterol, resting heart rate and body mass index. These disparate attributes controlling the heart risk factor are jointly contributed as the input variables to the presented fuzzy inference unit. The deployed sample record comprising of the particular heart patients' health data informatics is effectively enforced by the fuzzy control scheme and is consequently analyzed through the rigorous simulation experiments. Wide range of error and fairness indicators are employed for corroborating the accuracy estimation of the fuzzy output parameter of heart disease risk level. Specifically, we applied the normalized root-mean-square error, mean Poisson deviance, absolute logarithmic quotient error, relative absolute error, symmetric mean absolute deviation, entropy and Jain's fairness index for empirical substantiation of the proposed method in relation to the actual heart disease observational statistics. From the simulation analysis results, it was established that the optimal solution is obtained with the normalized root-mean-square error of 0.459 and mean Poisson deviance of 0.244 error approximations. In addition, the absolute log quotient error, relative absolute error, and symmetric mean absolute deviation measures are accordingly computed as 0.73, 4.6, and 0.65. Besides, the Shannon entropy and fairness indices for the statistical analysis of the proposed soft computing based biomedical application are relatively improved by almost 3.337 and 13.24% as compared to the actual heart disease dataset under surveillance.

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Use of wireless system in healthcare for developing countries

Cited by 14 publications

References 24 publications

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

Performance issues in wireless body area networks for the healthcare application: a survey and future prospects

A Development of LBS by Using Beacon BLE Technology in Healthcare

Multivariate Fuzzy Logic Based Smart Healthcare Monitoring for Risk Evaluation of Cardiac Patients

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