Noncontact Sensing of Contagion

Khanam, Fatema-Tuz-Zohra; Chahl, Loris A.; Chahl, Jaswant S.; Al-Naji, Ali; Perera, Asanka G.; Wang, Danyi; Lee, Y.H.; Ogunwa, Titilayo; Teague, Samuel; Nguyen, Tran Xuan Bach; McIntyre, Timothy D.; Pegoli, Simon P.; Tao, Yiting; McGuire, J.L.; Huynh, Jasmine; Chahl, Javaan

doi:10.3390/jimaging7020028

Cited by 13 publications

(10 citation statements)

References 159 publications

(227 reference statements)

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“…Unlike reflected visible light, thermal landmarks data are highly influenced by uncontrolled external sources. For example, as shown in [ 47 ], thermal data can be influenced by relative humidity, air convection, reflective surface and other heat sources such as the sun. As a result, thermal landmarks can be vastly different from early morning to late afternoon from which it is reasonable to expect problems with feature based tracking algorithms such as SLAM and optical flow.…”

Section: Discussionmentioning

confidence: 99%

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A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

2021

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Limited navigation capabilities of many current robots and UAVs restricts their applications in GPS denied areas. Large aircraft with complex navigation systems rely on a variety of sensors including radio frequency aids and high performance inertial systems rendering them somewhat resistant to GPS denial. The rapid development of computer vision has seen cameras incorporated into small drones. Vision-based systems, consisting of one or more cameras, could arguably satisfy both size and weight constraints faced by UAVs. A new generation of thermal sensors is available that are lighter, smaller and widely available. Thermal sensors are a solution to enable navigation in difficult environments, including in low-light, dust or smoke. The purpose of this paper is to present a comprehensive literature review of thermal sensors integrated into navigation systems. Furthermore, the physics and characteristics of thermal sensors will also be presented to provide insight into challenges when integrating thermal sensors in place of conventional visual spectrum sensors.

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Section: Discussionmentioning

confidence: 99%

“…In addition to the material of emitting objects, other factors that should be considered include the temperature, humidity, reflective surfaces and convection airflow of the intended operational environments [ 47 ]. This has substantial implications for navigation in natural environments.…”

Section: Physics Of Thermal Sensorsmentioning

confidence: 99%

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

2021

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“…Beside being studied for autonomous navigation, thermal imaging has been used to monitor railway infrastructure [ 36 ], monitor crops [ 37 , 38 , 39 ], driver monitoring [ 40 ], face recognition [ 41 ], vital sign extraction [ 42 ] and for COVID-19 detection [ 43 , 44 ] just to name a few.…”

Section: Related Workmentioning

confidence: 99%

Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging

Nguyen

Chahl

2022

J. Imaging

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This study is inspired by the widely used algorithm for real-time optical flow, the sparse Lucas–Kanade, by applying a feature extractor to decrease the computational requirement of optical flow based neural networks from real-world thermal aerial imagery. Although deep-learning-based algorithms have achieved state-of-the-art accuracy and have outperformed most traditional techniques, most of them cannot be implemented on a small multi-rotor UAV due to size and weight constraints on the platform. This challenge comes from the high computational cost of these techniques, with implementations requiring an integrated graphics processing unit with a powerful on-board computer to run in real time, resulting in a larger payload and consequently shorter flight time. For navigation applications that only require a 2D optical flow vector, a dense flow field computed from a deep learning neural network contains redundant information. A feature extractor based on the Shi–Tomasi technique was used to extract only appropriate features from thermal images to compute optical flow. The state-of-the-art RAFT-s model was trained with a full image and with our proposed alternative input, showing a substantial increase in speed while maintain its accuracy in the presence of high thermal contrast where features could be detected.

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“…According to their particular requirements, specialized medical equipment is used to estimate their physiological condition [ 4 ]. The vital signs monitored generally include respiratory rate (RR), heart rate (HR), temperature (T), blood pressure (BP), and oxygen saturation level (SpO 2 ) [ 5 , 6 ]. A very high or low heart rate may direct an underlying situation such as pain, infection, or illness.…”

Section: Introductionmentioning

confidence: 99%

“…A very high or low heart rate may direct an underlying situation such as pain, infection, or illness. Irregular values of respiratory rate may indicate hypercapnia, hypoxaemia, or acidosis [ 6 , 7 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks

et al. 2021

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Infants with fragile skin are patients who would benefit from non-contact vital sign monitoring due to the avoidance of potentially harmful adhesive electrodes and cables. Non-contact vital signs monitoring has been studied in clinical settings in recent decades. However, studies on infants in the Neonatal Intensive Care Unit (NICU) are still limited. Therefore, we conducted a single-center study to remotely monitor the heart rate (HR) and respiratory rate (RR) of seven infants in NICU using a digital camera. The region of interest (ROI) was automatically selected using a convolutional neural network and signal decomposition was used to minimize the noise artefacts. The experimental results have been validated with the reference data obtained from an ECG monitor. They showed a strong correlation using the Pearson correlation coefficients (PCC) of 0.9864 and 0.9453 for HR and RR, respectively, and a lower error rate with RMSE 2.23 beats/min and 2.69 breaths/min between measured data and reference data. A Bland–Altman analysis of the data also presented a close correlation between measured data and reference data for both HR and RR. Therefore, this technique may be applicable in clinical environments as an economical, non-contact, and easily deployable monitoring system, and it also represents a potential application in home health monitoring.

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Noncontact Sensing of Contagion

Cited by 13 publications

References 159 publications

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

Sparse Optical Flow Implementation Using a Neural Network for Low-Resolution Thermal Aerial Imaging

Non-Contact Automatic Vital Signs Monitoring of Infants in a Neonatal Intensive Care Unit Based on Neural Networks

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