An Embedded Non-Contact Body Temperature Measurement System with Automatic Face Tracking and Neural Network Regression

Huang, Po Chin; Chang, Tzu‐Hsuan; Lee, Meng-Ju; Lin, Tzu-Min; Chung, Meng-Liang; Wu, Bing-Fei

doi:10.1109/cacs.2016.7973902

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…Extending the distance range from 50 to 100 centimetres was demonstrated by Huang et al [7] using a technique based on neural network regression. Since the automatic face tracking function depends on a well-focused human face, this is an important consideration whenever facial measurements are being taken.…”

Section: Related Workmentioning

confidence: 99%

Healthcare Monitoring using Machine Learning Based Data Analytics

Janani¹,

Subramanian²,

Karthik³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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In this paper, we develop a machine learning based healthcare monitoring and analytics from various Internet of Medical Things (IoMT) devices for possible prediction of cardiovascular risk in patients. The study uses random forest for feature selection and then the fuzzy logic classifier is used for prediction of Cardio Vascular Disease (CVD). The simulation is conducted to test the efficacy of the proposed machine learning based data analytics model over various other methods. The results show than the proposed method has higher rate of classification accuracy in classifying the CVD with higher recall and F1-score than other methods.

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Section: Related Workmentioning

confidence: 99%

Healthcare Monitoring using Machine Learning Based Data Analytics

Janani¹,

Subramanian²,

Karthik³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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“…The automatic face tracking feature requires the human face should be focused appropriately while measuring. The information and results can also be viewed through App and Web [15]. Rahaman et al discussed the different types of smart health monitoring systems and focused on the advantages and shortcomings of the technologies deployed in health care systems [16].…”

Section: Related Workmentioning

confidence: 99%

IoT-Based Smart Health Monitoring System for COVID-19

Bhardwaj¹,

Joshi²,

Gaur

2022

SN COMPUT. SCI.

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With the commencement of the COVID-19 pandemic, social distancing and quarantine are becoming essential practices in the world. IoT health monitoring systems prevent frequent visits to doctors and meetings between patients and medical professionals. However, many individuals require regular health monitoring and observation through medical staff. In this proposed work, we have taken advantage of the technology to make patients life easier for earlier diagnosis and treatment. A smart health monitoring system is being developed using Internet of Things (IoT) technology which is capable of monitoring blood pressure, heart rate, oxygen level, and temperature of a person. This system is helpful for rural areas or villages where nearby clinics can be in touch with city hospitals about their patient health conditions. However, if any changes occur in a patient's health based on standard values, then the IoT system will alert the physician or doctor accordingly. The maximum relative error (%ϵ r ) in the measurement of heart rate, patient body temperature and SPO 2 was found to be 2.89%, 3.03%, 1.05%, respectively, which was comparable to the commercials health monitoring system. This health monitoring system based on IoT helps out doctors to collect real-time data effortlessly. The availability of high-speed internet allows the system to monitor the parameters at regular intervals. Furthermore, the cloud platform allows data storage so that previous measurements could be retrieved in the near future. This system would help in identifying and early treatment of COVID-19 individual patients.

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“…Several papers propose a temperature monitoring system in detecting body temperature. Alkhayat et al (2015) and Huang et al (2016) suggested a fever detection system that uses multiple infrared cameras, which allowed the room temperature to be controlled and identify students that have a temperature higher than 37 ℃ (Alkhayat et al, 2015;Huang et al, 2016). Apart from that, Somboonkaew et al (2017) and Haghmohammadi et al (2018) proposed the usage of thermal imaging from IR cameras.…”

Section: Introductionmentioning

confidence: 99%

Development of Attendance and Temperature Monitoring System using IoT with Wireless Power Transfer Application

Abdullah¹,

Shazali²

2022

JST

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Enclosed areas pose a greater risk of transmitting infectious and bacterial diseases. The proposed system helps prevent disease by tracking students’ daily body temperature before entering the school premises. Each student will be provided with a unique QR code containing the student information, such as their name and class. The QR code needs to be scanned first by the camera-equipped smartphone before reading the body temperature. The thermometer will record the student’s body temperature and send the information to the smartphone via Bluetooth. The student’s profile will be updated with the recorded daily temperature. An Android application will be developed to scan the QR code and display the students’ profiles and information. In order to design a battery-less system, the system will be integrated with a wireless power transfer circuit. Based on the simulation results, the wireless power transfer circuit can be used as a wireless charger for the smartphone used in the system or for charging the thermometer’ of the thermometer.

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An Embedded Non-Contact Body Temperature Measurement System with Automatic Face Tracking and Neural Network Regression

Cited by 10 publications

References 11 publications

Healthcare Monitoring using Machine Learning Based Data Analytics

Healthcare Monitoring using Machine Learning Based Data Analytics

IoT-Based Smart Health Monitoring System for COVID-19

Development of Attendance and Temperature Monitoring System using IoT with Wireless Power Transfer Application

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