Development of an Automatic Contactless Thermometer Alert System Based on GPS and Population Density
Published online first on April 6, 2021.
In today's out-breaking Covid-19 circumstance, treatments are preferred to be contactless. Social distancing has become a mandate in order to prevent disease spreading. In such a scenario, checking the body temperature is preferable to be made contactless because it helps the doctors and social workers to stay away from the symptomatic patients. Infrared (IR) contactless thermometers are employed in measuring the temperature while preventing direct contact with the body. Improved functionalities in the contactless thermometer can provide accurate precision in measurements and calculations. Technological advancement in pharmacy has cohesively improved over time. Coupling Machine Learning (CML) will revolutionize the process of testing. The demand for automated temperature test equipment is likely to grow at a significant pace, with the continuous advancements in technology and the adoption of ATE (Automated Test Equipment). The Global Positioning System (GPS) easy tracking and navigation can be used for easy tracking. Population density can be used to calculate the amount of population in a particular area. The proposed automatic contact-less thermometer system has the potential to replace the traditional temperature measuring techniques and safeguard from human-to-human transmission diseases.
Keywords:Covid-19, IR technology, ATE, GPS, population density
R. Maddison and C. Ni Mhurchu, "Global positioning system: a new opportunity in physical activity measurement," International Journal of Behavioral Nutrition and Physical Activity, vol. 6, no. 1, Nov. 2009, Art. no. 73. https://doi.org/10.1186/1479-5868-6-73
F. Abulude, A. Akinnusotu, and A. Adeyemi, "Global Positioning System and It's Wide Applications," Continental Journal of Information Technology, vol. 9, no. 1, pp. 22-32, Oct. 2015.
"Population Density: Definition, Formula & Examples - Video & Lesson Transcript," Study.com. https://study.com/academy/lesson/population-density-definition-formula-examples.html (accessed Mar. 21, 2021).
J. F. McDonald, "Econometric studies of urban population density: A survey," Journal of Urban Economics, vol. 26, no. 3, pp. 361-385, Nov. 1989. https://doi.org/10.1016/0094-1190(89)90009-0
J.-H. Min, N. Gelo, and H. Jo, "Non-contact and Real-time Dynamic Displacement Monitoring using Smartphone Technologies," Life Cycle Reliability and Safety Engineering, vol. 4, no. 2, pp. 40-51, May 2015.
T. M. Fernández-Caramés and P. Fraga-Lamas, "Towards The Internet of Smart Clothing: A Review on IoT Wearables and Garments for Creating Intelligent Connected E-Textiles," Electronics, vol. 7, no. 12, p. 405, Dec. 2018. https://doi.org/10.3390/electronics7120405
P. F. Paradis and W. Rhim, "Non-Contact Measurements of Thermophysical Properties of Titanium at High Temperature," Journal of Chemical Thermodynamics, vol. 32, no. 1, pp. 123-133, Mar. 1999. https://doi.org/10.1006/jcht.1999.0576
V. Siva Brahmaiah, B. Rajkiran, and S. Pradeep, "An Intellectual Dual Axis Efficient Solar Tracking System by Using IoT Integrated Controller," SSRN.
S. K. Kumaravel et al., "Investigation on the impacts of COVID-19 quarantine on society and environment: Preventive measures and supportive technologies," 3 Biotech, vol. 10, no. 9, Sep. 2020, Art. no. 393. https://doi.org/10.1007/s13205-020-02382-3
S. M. Basha and D. S. Rajput, "Chapter 9 - Survey on Evaluating the Performance of Machine Learning Algorithms: Past Contributions and Future Roadmap," in Deep Learning and Parallel Computing Environment for Bioengineering Systems, A. K. Sangaiah, Ed. Academic Press, 2019, pp. 153-164. https://doi.org/10.1016/B978-0-12-816718-2.00016-6
E. Moisello, M. Vaiana, M. E. Castagna, G. Bruno, P. Malcovati, and E. Bonizzoni, "An Integrated Micromachined Thermopile Sensor With a Chopper Interface Circuit for Contact-Less Temperature Measurements," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 66, no. 9, pp. 3402-3413, Sep. 2019. https://doi.org/10.1109/TCSI.2019.2928717
B. Trstenjak, D. Donko, and Z. Avdagic, "Adaptable Web Prediction Framework for Disease Prediction Based on the Hybrid Case Based Reasoning Model," Engineering, Technology & Applied Science Research, vol. 6, no. 6, pp. 1212-1216, Dec. 2016. https://doi.org/10.48084/etasr.753
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