Temperature-compensated infrared-based low-cost mobile platform module for mass human temperature screening

Somboonkaew, Armote; Vuttivong, Sirajit; Prempree, Panintorn; Amarit, Ratthasart; Chanhorm, Sataporn; Chaitavon, Kosom; Porntheeraphat, Supanit; Sumriddetchkajorn, Sarun

doi:10.1364/ao.388313

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…The IRT experiments found in this review reported sensitivity values between 15% and 100%. However, it should be noted that the studies that reported 100% sensitivity used NCIT systems as the reference [41]. The highest sensitivity for a study using a contact-based reference was 91% [42].…”

Section: Resultsmentioning

confidence: 99%

Technologies for Fever Screening in the Time of COVID-19: A Review

et al. 2022

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During the COVID-19 pandemic, there has been an increasing rollout of non-contact fever screening solutions to assist in curbing the spread of disease. This study begins by describing how screening for disease has historically been performed. It proposes four measurement characteristics of an ideal screening solution: non-contact, effective, rapid and lowcost measurements. Next, it reviews the existing literature on fever-screening using non-contact infrared thermometer (NCIT) devices as well as infrared thermography (IRT) devices, as these are two technologies which have experienced increasing use. For this review, 185 research papers were identified, 21 research studies were included after inclusion and exclusion criteria were applied. A total of 35 experiments were identified for analysis and their results tabulated. Of these studies, 66% are IRT and 34% are NCIT, with a median sample size of 430 subjects. 26 experiments involve febrile participants, with a median febrile percentage of 11.22 % of population. The reported sensitivity of febrile detection using NCIT varies from 3.7% to 97% and when using IRT it varies from 15% to 100%. Both indoor and outdoor studies are investigated, as well as those conducted in acute and non-acute settings. The results of this review show a clear lack of consensus on the effectiveness of these systems. Overall, these results indicate that sensitivity and specificity are reduced when using IRT and NCIT technologies compared to other thermometers used in medical practice. Their use should be carefully assessed based on the risks present in each particular measurement scenario.

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Section: Resultsmentioning

confidence: 99%

Technologies for Fever Screening in the Time of COVID-19: A Review

et al. 2022

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“…IRT can be used to quickly screen the body temperature of large groups of people. For example, Armote Somboonkaew's research shows that IRT can screen the body temperature of nine people simultaneously at a speed of eight frames/second [49]. A correlation coefficient of 0.731 was found between IRT and forehead skin thermometers.…”

Section: Discussionmentioning

confidence: 99%

Non-Contact Infrared Thermometers and Thermal Scanners for Human Body Temperature Monitoring: A Systematic Review

Zhao,

Bergmann

2023

Sensors

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In recent years, non-contact infrared thermometers (NCITs) and infrared thermography (IRT) have gained prominence as convenient, non-invasive tools for human body temperature measurement. Despite their widespread adoption in a range of settings, there remain questions about their accuracy under varying conditions. This systematic review sought to critically evaluate the performance of NCITs and IRT in body temperature monitoring, synthesizing evidence from a total of 72 unique settings from 32 studies. The studies incorporated in our review ranged from climate-controlled room investigations to clinical applications. Our primary findings showed that NCITs and IRT can provide accurate and reliable body temperature measurements in specific settings and conditions. We revealed that while both NCITs and IRT displayed a consistent positive correlation with conventional, contact-based temperature measurement tools, NCITs demonstrated slightly superior accuracy over IRT. A total of 29 of 50 settings from NCIT studies and 4 of 22 settings from IRT studies achieved accuracy levels within a range of ±0.3 °C. Furthermore, we found that several factors influenced the performance of these devices. These included the measurement location, the type of sensor, the reference and tool, individual physiological attributes, and the surrounding environmental conditions. Our research underscores the critical need for further studies in this area to refine our understanding of these influential factors and to develop standardized guidelines for the use of NCITs and IRT.

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“…However, these models do not scale well, and there is a high risk of bias that raises concerns to be used in daily practice. To mitigate this, [32], use multiple sensors to improve accuracy. Various methods to fuse different algorithms have also been explored in [24,25].…”

Section: Ai Models For Covid Prognosis and Diagnosismentioning

confidence: 99%

SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation

Narayanan¹,

Manoghar²,

Prashanth³

et al. 2020

Preprint

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Amodal recognition is the ability of the system to detect occluded objects. Most state-of-the-art Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in real-world applications, such as dynamic objects. We propose SeekNet , an improved optimization method for amodal recognition through embodied visual recognition. Additionally, we implement SeekNet for social robots, where there are multiple interactions with crowded humans. Hence, we focus on occluded human detection & tracking and showcase the superiority of our algorithm over other baselines. We also experiment with SeekNet to improve the confidence of COVID-19 symptoms pre-screening algorithms using our efficient embodied recognition system.

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Temperature-compensated infrared-based low-cost mobile platform module for mass human temperature screening

Cited by 10 publications

References 8 publications

Technologies for Fever Screening in the Time of COVID-19: A Review

Technologies for Fever Screening in the Time of COVID-19: A Review

Non-Contact Infrared Thermometers and Thermal Scanners for Human Body Temperature Monitoring: A Systematic Review

SeekNet: Improved Human Instance Segmentation and Tracking via Reinforcement Learning Based Optimized Robot Relocation

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