Smart Healthcare

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Smart Healthcare
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CASE STUDY
The Cognitive Internet of Things (IoT), which connects with intelligent machines,
sensors, and other healthcare stakeholders, takes wise decisions based on patient status, offers
prompt, cost-effective, and open services. In a case study, a deep-learning EEG seizure
prediction system also provides insight into the IoT-cloud cognitive intelligent health
platform[ CITATION Cha18 \l 1033 ]. In the system suggested for the recording and
transmitting EEG signals from people with epilepsy, we use smart EEG sensors (except smart
healthcare sensors). The cognitive system then decides whether to forward the data to the
profound learning module in real-time and on potential tasks.
The suggested system incorporates the motions, actions, and facial features of the patient
to assess the patient's condition. Signal recognition and seizure senses occur in the cloud when
signals with a probability score are labeled as seizure or non-seizure. The findings are passed to
healthcare professionals or other partners who will track patients and make the right decisions to
support the patient in emergencies[ CITATION Hos191 \l 1033 ]. Experimental findings indicate
that 99,2% and 93,5% respectively of the proposed model are accurate and responsive.
Besides, IoT and cloud technology incorporation offered a streamlined and omniprésent
environment for intelligent surveillance. Residents of smart cities now have connections to new
mobile technology and smart sensor device. Specialized physicians, primary care centers, and
clinics are impossible to locate in the world of such a smart community. It is often very
dangerous to switch patients in critical situations. Therefore, we ought to develop a system for
clever health surveillance integrated with existing tools to demonstrate that health facilities are
quality and accessible. Via a smart system for clinical surveillance, multimedia medical signals
can be transmitted and processed through smart sensors and mobile devices to provide patients
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with rapid support and better healthcare[ CITATION Isl17 \l 1033 ]. However, due to their
sophistication, these health details and signs are also normal and difficult to navigate.
FUNCTIONALITY
The objectives suggested by intelligent cities are to increase residents' quality of living by
upgrading municipal facilities, fostering creativity, and enhancing public healthcare. A literary
study on using big data analysis and BDA technology to provide patients residing in intelligent
communities with extremely effective medical and health care facilities[ CITATION Cha18 \l
1033 ]. Many Big Data Analytics solutions require data mining, so we propose a straightforward
way to gather data relating to medicine and health from patients utilizing a set of sensors and a
cloud-based approach to collecting, transmission, and analysis of data using a (hypothetical)
learning framework.
To conclude, we may suggest that health systems in smart cities and the use of big data
mining are (most) largely theoretical at this stage for enhancing medical decision
support[ CITATION Isl17 \l 1033 ]. These services already seem fine on paper, but a genuinely
successful model is yet to be tried and unanimously recognized as successful in actual clinical
practice.
As one of the most important sectors with enormous demands, the healthcare sector
emerged. In addition to delivering critical and critical care to patients, this business generates
high profits for the government and the corporate sector[ CITATION Cha18 \l 1033 ]. A rivalry
among different health providers in providing mature and advanced facilities and devices has
recently been witnessed in the intelligentsia industry

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TECHNICAL DETAILS OF THE IMPLEMENTATION
Diagnosis and wellbeing monitoring in the health sector is a very critical activity.
Because of the time limit, people should not attend clinics that might contribute to more health
problems at once. Much of the health programs have been designed to anticipate and diagnose
the wellbeing of patients, but they can even track their health at daily rates. Many findings
suggest that early warning is the safest approach to cure wellbeing since early detection helps
people recognize their health condition and alert them.
Health is the world's most populous country, particularly India, where the majority of
them live continuously and consistently in villages without health facilities in real-time. With the
increased usage of technology, such a smart health surveillance framework is urgently needed,
which can interact between network equipment and application to support patients and doctors
control, track and archive confidential patient data that include medical details[ CITATION
Jar20 \l 1033 ]. This paper shows the concept of solving problems across the Internet of Things
utilizing the new technologies (IoT). It introduces the architectonic study of intelligent healthcare
across the Internet of Things(IoT) to ensure better healthcare for everyone.
The surveillance infrastructure for clinical services in hospitals and many other health
centers has increased significantly. Today, many countries around the world are concerned with
wearable healthcare control systems with new technology. The introduction of IoT technology
enables healthcare to progress from face-to-face consultation to telemedicine. The paper presents
a smart IoT-based healthcare device that can track the specific health signals of the patient and
the room status of patients in real-time[ CITATION Pra171 \l 1033 ]. Five instruments are used
in this device to collect data from hospitals called heartbeat sensors, the body temperature
monitor, the room temperature sensor, the CO sensor, and the CO2 sensor. The error percentage
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of the established scheme for each case is below a certain limit (< 5%). A network transmits a
patient's diagnosis to medical personnel, where the actual patient state can be processed and
analyzed. The created prototype is ideal for measuring the health care system's performance.
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REFERENCES
Chaudhary, R., Jindal, A., Aujla, G. S., Kumar, N., Das, A. K., & Saxena, N. (2018). Lscsh:
Lattice-based secure cryptosystem for smart healthcare in smart cities environment. IEEE
Communications Magazine, 56(4), 24-32.
Hossain, M. S., Muhammad, G., & Alamri, A. (2019). Smart healthcare monitoring: a voice
pathology detection paradigm for smart cities. Multimedia Systems, 25(5), 565-575.
Islam, M. M., Razzaque, M. A., Hassan, M. M., Ismail, W. N., & Song, B. (2017). Mobile cloud-
based big healthcare data processing in smart cities. IEEE Access, 5, 11887-11899.
Jararweh, Y., Al-Ayyoub, M., & Benkhelifa, E. (2020). An experimental framework for future
smart cities using data fusion and software-defined systems: the case of environmental
monitoring for smart healthcare. Future Generation Computer Systems, 107, 883-897.
Pramanik, M. I., Lau, R. Y., Demirkan, H., & Azad, M. A. K. (2017). Smart health: Big data-
enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370-
383.

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profound learning module in real-time and on potential tasks.

cloud-based approach to collecting, transmission, and analysis of data using a (hypothetical)

recently been witnessed in the intelligentsia industry

TECHNICAL DETAILS OF THE IMPLEMENTATION

people recognize their health condition and alert them.

across the Internet of Things(IoT) to ensure better healthcare for everyone.

Lattice-based secure cryptosystem for smart healthcare in smart cities environment. IEEE

Communications Magazine, 56(4), 24-32.

pathology detection paradigm for smart cities. Multimedia Systems, 25(5), 565-575.

based big healthcare data processing in smart cities. IEEE Access, 5, 11887-11899.

monitoring for smart healthcare. Future Generation Computer Systems, 107, 883-897.

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