scholarly journals ADDSMART: Address digitization and smart mailbox with RFID technology

2016 ◽  
Author(s):  
Jonathan Ross Tew ◽  
Lydia Ray
Keyword(s):  
Rfid Technology

A Multi-Sensing-Range Method for Efficient Position Estimation by Passive RFID Technology

2009 ◽  
Vol E92-A (10) ◽  
pp. 2609-2617 ◽  
Author(s):  
Toshihiro HORI ◽  
Tomotaka WADA ◽  
Norie UCHITOMI ◽  
Kouichi MUTSUURA ◽  
Hiromi OKADA
Keyword(s):  
Position Estimation ◽  
Rfid Technology ◽  
Sensing Range ◽  
Passive Rfid

Development of the On-board Maintenance Management System for Ground Coils of Maglev by Using RFID Technology

2012 ◽  
Vol 53 (1) ◽  
pp. 46-51
Author(s):  
Minoru TANAKA ◽  
Noriyuki TAKAHASHI ◽  
Masao SUZUKI ◽  
Ryohei IKEDA ◽  
Sei NAGASAKA
Keyword(s):  
Management System ◽  
Maintenance Management ◽  
Rfid Technology

Field Trials of RFID Technology for Tracking Pre-Fabricated Pipe Spools

2004 ◽  
Author(s):  
Jongchul Song ◽  
Carlos Caldas ◽  
Esin Ergen ◽  
Carl Haas ◽  
Burcu Akinci
Keyword(s):  
Field Trials ◽  
Rfid Technology

scholarly journals Smart Gateless System using RFID Technology in Universiti Malaysia Perlis

2021 ◽  
Vol 1878 (1) ◽  
pp. 012066
Author(s):  
A F M Fazilah ◽  
M Jusoh ◽  
A Zakaria ◽  
T Sabapathy ◽  
M F Ibrahim ◽  
...  
Keyword(s):  
Rfid Technology

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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