Poster Abstract: An Implementation of an Internet of Things System for Smart Hospitals

2020 ◽  
Author(s):  
Jichao Leng ◽  
Zihuai Lin ◽  
Peng Wang
Keyword(s):  
Internet Of Things ◽  
Poster Abstract ◽  
Smart Hospitals

ElHealth: Using Internet of Things and data prediction for elastic management of human resources in smart hospitals

2020 ◽  
Vol 87 ◽  
pp. 103285 ◽  
Author(s):  
Gabriel Souto Fischer ◽  
Rodrigo da Rosa Righi ◽  
Gabriel de Oliveira Ramos ◽  
Cristiano André da Costa ◽  
Joel J.P.C. Rodrigues
Keyword(s):  
Internet Of Things ◽  
Human Resources ◽  
Data Prediction ◽  
Smart Hospitals

scholarly journals Application of IoT in the COVID-19 Pandemic

2021 ◽  
Vol 5 (5) ◽  
pp. 1-3
Author(s):  
B. Shoban Babu ◽  
Prince Patel
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Time Data ◽  
Smart Systems ◽  
Risk Patients ◽  
Review Study ◽  
Smart Hospitals ◽  
High Level ◽  
The Internet Of Things ◽  
Level Of Agreement

As we all know, the Internet has altered everything, and the Internet of Things has given us hope for a bright future of the Internet with Machine-to-Machine (M2M) connectivity. This review study demonstrated that smart systems based on the Internet of Things are feasible and economical to build (IoT). In the field of healthcare, the Internet of Things has made significant progress. This article examines how the Internet of Things (IoT) is revolutionising the healthcare industry by giving huge healthcare advantages to humanity through accessible and practical healthcare solutions specially during the hard coronavirus situation around the world. The purpose of this study is to address the function of IoT in smart hospitals, as well as its importance in dealing with pandemics. Various smart gadgets that can provide a variety of features, such as adequate monitoring of high-risk patients, tracking their bio-metric measurements, and gathering real-time data, can be used to serve community-specific demands during pandemic spread. We've also looked into other plans that can detect unforeseen events utilising a variety of sensors and display the information gathered on an LED display. The results of observational studies have indicated a high level of agreement with the hypothetical claims.

scholarly journals Smart Hospitals E-Medico Management System

2019 ◽  
Vol 8 (3) ◽  
pp. 4651-4655
Keyword(s):  
Internet Of Things ◽  
Death Rate ◽  
Critical Level ◽  
Threshold Value ◽  
Health Data ◽  
Iot Platforms ◽  
Smart Hospitals ◽  
Different Levels ◽  
Level Monitoring

As for medical plane, there are some exertions has to be prevailed over for making the quality of treatment to be elevated. So there are two different levels of changes has been concentrated to enhance the level of hospitalization. The patient’s height, weight, temperature, pulse rate are being checked, manually by nurses. This is a time-consuming and tedious process for patients. Patient’s Health Data Assortment (PHDA) is the device to be initialized in all the hospitals where the patients can fill up their details, and problems they face. Simultaneously it is indicated to doctor through the app. The next level of concentration will be on sensing the drips level sensing. We use saline to improve patient’s health and avoid dehydration. The patient should be monitored and given special care during this stage or period of time. The saline level of the patient should be checked regularly. There are many deaths happening because of carelessness of caretakers and lack of nurses and doctors towards saline completion. If the saline level is monitored automatically the death rate can be reduced. The saline level monitoring system is developed to protect patient’s life from this type of accident. This system is build using Internet of Things (IOT) platforms. A predefined critical level is fed into the system and compared with actual level of the saline in the patient. When the level reaches the threshold value a buzzer and a alter message will send to the corresponding nurse or doctors for replacement of bottle.

scholarly journals Machine Learning based Activity learning for Behavioral Contexts in Internet of Things

2021 ◽  
Vol 33 (1) ◽  
pp. 47-58
Author(s):  
Muhammad Safyan ◽  
Sohail Sarwar ◽  
Zia Ul Qayyum ◽  
Muddessar Iqbal ◽  
Shancang Li ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Activity Recognition ◽  
Daily Life ◽  
Vital Role ◽  
Sensor Data ◽  
Learning Activity ◽  
Behavioral Models ◽  
Ontology Evolution ◽  
Smart Hospitals ◽  
Artificial Neural Network Ann

Ontology based activity learning models play a vital role in diverse fields of Internet of Things (IoT) such as smart homes, smart hospitals or smart communities etc. The prevalent challenges with ontological models are their static nature and inability of self-evolution. The models cannot be completed at once and smart home inhabitants cannot be restricted to limit their activities. Also, inhabitants are not predictable in nature and may perform “Activities of Daily Life (ADL)” not listed in ontological model. This gives rise to the need of developing an integrated framework based on unified conceptual backbone (i.e. activity ontologies), addressing the lifecycle of activity recognition and producing behavioral models according to inhabitant’s routine. In this paper, an ontology evolution process has been proposed that learns particular activities from existing set of activities in daily life (ADL). It learns new activities that have not been identified by the recognition model, adds new properties with existing activities and learns inhabitant’s newest behavior of performing activities through Artificial Neural Network (ANN). The better degree of true positivity is evidence of activity recognition with detection of noisy sensor data. Effectiveness of proposed approach is evident from improved rate of activity learning, activity detection and ontology evolution.

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