Remote Patient Monitoring with Health Care Records Using Iot

2019 ◽  
pp. 87-97
Author(s):  
Valan Abisha J ◽  
Abisha C ◽  
Abirami S ◽  
Reziga Susia J.
Keyword(s):  
Medical Records ◽  
Patient Monitoring ◽  
Open Data ◽  
Personal Digital Assistant ◽  
Remote Patient Monitoring ◽  
Novel Approach ◽  
Decision Supporting ◽  
Remote Patient ◽  
Health Care Records

For a medical treatment with IoT-based facilities, physicians always have to pay much more attentions to the raw medical records of target patients instead of directly making medical advice, conclusions or diagnosis from their experiences. Because the medical records in IoT-based hospital information system (HIS) are dispersedly obtained from distributed devices such as tablet computer, personal digital assistant, automated analyzer, and other medical devices, they are raw, simple, weak-content, and massive. Such medical records cannot be used for further analyzing and decision supporting due to that they are collected in a weak-semantic manner. In this paper, we propose a novel approach to enrich IoT-based medical records by linking them with the knowledge in linked open data. A case study is conducted on a real-world IoT-based HIS system in association with our approach, the experimental results show that medical records in the local HIS system are significantly enriched and useful for healthcare analysis and decision making, and further demonstrate the feasibility and effectiveness of our approach for knowledge accessing.

Low-power radio telemetry: the potential for remote patient monitoring

1996 ◽  
Vol 2 (4) ◽  
pp. 185-191 ◽  
Author(s):  
W G Scanlon ◽  
N E Evans ◽  
G C Crumley ◽  
Z M Mccreesh
Keyword(s):  
Patient Monitoring ◽  
Radio Telemetry ◽  
Vital Signs ◽  
Clinical Results ◽  
Full Duplex ◽  
Remote Patient Monitoring ◽  
Monitoring Equipment ◽  
Future Developments ◽  
Remote Patient

Radio-based signalling devices will play an important role in future generations of remote patient monitoring equipment, both at home and in hospital. Ultimately, it will be possible to sample vital signs from patients, whatever their location and without them necessarily being aware that a measurement is being taken. This paper reviews current methods for the transmission by radio of physiological parameters over ranges of 0.3, 3 and 30 m, and describes the radiofrequency hardware required and the carrier frequencies commonly used. Future developments, including full duplex systems and the use of more advanced modulation schemes, are described. The paper concludes with a case study of a human temperature telemeter built to indicate ovulation. Clinical results clearly show the advantage to be had in adopting radio biotelemetry in this instance.

scholarly journals How to Design a Remote Patient Monitoring System? A French Case Study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Marie Ferrua ◽  
Etienne Minvielle ◽  
Aude Fourcade ◽  
Benoît Lalloué ◽  
Claude Sicotte ◽  
...  
Keyword(s):  
Monitoring System ◽  
Patient Monitoring ◽  
Remote Patient Monitoring ◽  
System A ◽  
French Case ◽  
Patient Monitoring System ◽  
Remote Patient

Implementing Internet of Things in a Remote Patient Medical Monitoring System

2018 ◽  
Vol 6 (8) ◽  
pp. 159 ◽  
Author(s):  
Ifeoma V. Ngonadi
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Medical Records ◽  
Research Work ◽  
Healthcare Delivery ◽  
Vital Signs ◽  
Personal Digital Assistant ◽  
Computer Applications ◽  
Medical Monitoring ◽  
Remote Patient

The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Remote patient monitoring enables the monitoring of patients’ vital signs outside the conventional clinical settings which may increase access to care and decrease healthcare delivery costs. This paper focuses on implementing internet of things in a remote patient medical monitoring system. This was achieved by writing two computer applications in java in which one simulates a mobile phone called the Intelligent Personal Digital Assistant (IPDA) which uses a data structure that includes age, smoking habits and alcohol intake to simulate readings for blood pressure, pulse rate and mean arterial pressure continuously every twenty five which it sends to the server. The second java application protects the patients’ medical records as they travel through the networks by employing a symmetric key encryption algorithm which encrypts the patients’ medical records as they are generated and can only be decrypted in the server only by authorized personnel. The result of this research work is the implementation of internet of things in a remote patient medical monitoring system where patients’ vital signs are generated and transferred to the server continuously without human intervention.

scholarly journals Remote Patient Monitoring Using Radio Frequency Identification (RFID) Technology and Machine Learning for Early Detection of Suicidal Behaviour in Mental Health Facilities

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 776
Author(s):  
Xiaohui Tao ◽  
Thanveer Basha Shaik ◽  
Niall Higgins ◽  
Raj Gururajan ◽  
Xujuan Zhou
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Radio Frequency Identification ◽  
Patient Monitoring ◽  
Vital Signs ◽  
Remote Patient Monitoring ◽  
Rfid Technology ◽  
Rpm System ◽  
Frequency Identification ◽  
Remote Patient

Remote Patient Monitoring (RPM) has gained great popularity with an aim to measure vital signs and gain patient related information in clinics. RPM can be achieved with noninvasive digital technology without hindering a patient’s daily activities and can enhance the efficiency of healthcare delivery in acute clinical settings. In this study, an RPM system was built using radio frequency identification (RFID) technology for early detection of suicidal behaviour in a hospital-based mental health facility. A range of machine learning models such as Linear Regression, Decision Tree, Random Forest, and XGBoost were investigated to help determine the optimum fixed positions of RFID reader–antennas in a simulated hospital ward. Empirical experiments showed that Decision Tree had the best performance compared to Random Forest and XGBoost models. An Ensemble Learning model was also developed, took advantage of these machine learning models based on their individual performance. The research set a path to analyse dynamic moving RFID tags and builds an RPM system to help retrieve patient vital signs such as heart rate, pulse rate, respiration rate and subtle motions to make this research state-of-the-art in terms of managing acute suicidal and self-harm behaviour in a mental health ward.

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