Influence of image resolution on the performance of remote breathing rate measurement using thermal imaging technique

Hu, Menghan; Zhai, Guangtao; Li, Duo; Li, Hanqi; Liu, Mengxin; Tang, Wencheng; Chen, Yuanchun

doi:10.1016/j.infrared.2018.05.026

Cited by 14 publications

(8 citation statements)

References 28 publications

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“…The temperature signals recorded by the thermal camera are directly similar to the pulse wave, but its shape is noisy, shifted, and smoothed due to the inclusion of targeted information as well as noise coming from the environment and other physiological signals. [27][28][29][30][31] The change in body surface temperature is the result of heat exchange by convection and conduction of blood vessels and the surrounding tissues in blood flow under the body surface. To resolve heart-related information such as heart rate, we established the bioheat transfer model.…”

Section: Thermal Imagingmentioning

confidence: 99%

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Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Zhang

et al. 2023

Advanced Intelligent Systems

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In recent years, optical imaging techniques have gained wide recognition for the measurement of vital signals, such as heart rate, respiratory rate, oxygen saturation, and blood pressure, which are crucial indicators for evaluating human health conditions in clinical examinations. There is a wide range of optical imaging methods for remote physiological signal monitoring, including RGB imaging, thermal imaging, hyperspectral imaging, depth imaging, and multimodal imaging, which provide spatial information compared to other noncontact measurement approaches, thereby allowing extensive applications in this area. In this survey, some fundamental knowledge about optical imaging methods for vital signal measurement is reviewed, including principles of various optical imaging techniques, processing methods for data analysis, discussion on advantages and disadvantages, application summary, and future prospects. This is a comprehensive overview of the noncontact physiological signal measurement of optical imaging approaches.

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Section: Thermal Imagingmentioning

confidence: 99%

Section: Fundamentals Of Optical Imaging Techniquesmentioning

confidence: 99%

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Zhang

et al. 2023

Advanced Intelligent Systems

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“…We define it as a "thermalfoot print". Thermal imaging is a passive imaging technique, and it can record the emitted energy from the objects whose absolute temperature is higher than zero without any external stimulation such as harmful radiation and illumination [30]. Considering the characteristic of far-infrared band, thermal imager is able to capture this thermal trace, so as to obtain a set of thermal sequences.…”

Section: Framework Of Thermalfootmentioning

confidence: 99%

Estimating Departure Time Using Thermal Camera and Heat Traces Tracking Technique

Wang

et al. 2020

Sensors

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Advancement in science and technology is playing an increasingly important role in solving difficult cases at present. Thermal cameras can help the police crack difficult cases by capturing the heat trace on the ground left by perpetrators, which cannot be spotted by the naked eye. Therefore, the purpose of this study is to establish a thermalfoot model using thermal imaging system to estimate the departure time. To this end, in the current work, we use a thermal camera to acquire the thermal sequence left on the floor, and convert it into the heat signal via image processing algorithm. We establish the model of thermalfoot print as we observe that the residual temperature would exponentially decrease with the departure time according to Newton’s Law of Cooling. The correlation coefficients of 107 thermalfoot models derived from the corresponding 107 heat signals are basically above 0.99. In a validation experiment, a residual analysis is conducted and the residuals between estimated departure time points and ground-truth times are almost within a certain range from −150 s to +150 s. The reverse accuracy of the thermalfoot model for estimating departure time at one-third, one-half, two-thirds, three-fourths, four-fifths, and five-sixths capture time points are 71.96%, 50.47%, 42.06%, 31.78%, 21.70%, and 11.21%, respectively. The results of comparison experiments with two subjective evaluation methods (subjective 1: we directly estimate the departure time according to obtained local curves; subjective 2: we utilize auxiliary means such as a ruler to estimate the departure time based on obtained local curves) further demonstrate the effectiveness of thermalfoot model for detecting the departure time inversely. Experimental results also demonstrated that the thermalfoot model has good performance on the departure time reversal within a short time window someone leaves, whereas it is probably only approximately 15% to accurately determine the departure time via thermalfoot model within a long time window someone leaves. The influence of outliers, ROI (Region of Interest) selection, ROI size, different capture time points and environment temperature on the performance of thermalfoot model on departure time reversal can be explored in the future work. Overall, the thermalfoot model can help the police solve crimes to some extent, which in turn brings more guarantees for people’s health, social security, and stability.

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“…However, high-resolution thermal imaging cameras are very expensive, and Hu (2018) showed that the performance of this approach decreases substantially when the resolution of a thermal image is 320 × 240 or less. Hence, it is a difficult method to use in general [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Non-Contact Respiration Measurement Method Based on RGB Camera Using 1D Convolutional Neural Networks

Hwang

Lee

2021

Sensors

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Conventional respiration measurement requires a separate device and/or can cause discomfort, so it is difficult to perform routinely, even for patients with respiratory diseases. The development of contactless respiration measurement technology would reduce discomfort and help detect and prevent fatal diseases. Therefore, we propose a respiration measurement method using a learning-based region-of-interest detector and a clustering-based respiration pixel estimation technique. The proposed method consists of a model for classifying whether a pixel conveys respiration information based on its variance and a method for classifying pixels with clear breathing components using the symmetry of the respiration signals. The proposed method was evaluated with the data of 14 men and women acquired in an actual environment, and it was confirmed that the average error was within approximately 0.1 bpm. In addition, a Bland–Altman analysis confirmed that the measurement result had no error bias, and regression analysis confirmed that the correlation of the results with the reference is high. The proposed method, designed to be inexpensive, fast, and robust to noise, is potentially suitable for practical use in clinical scenarios.

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Influence of image resolution on the performance of remote breathing rate measurement using thermal imaging technique

Cited by 14 publications

References 28 publications

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Recent Progress of Optical Imaging Approaches for Noncontact Physiological Signal Measurement: A Review

Estimating Departure Time Using Thermal Camera and Heat Traces Tracking Technique

Non-Contact Respiration Measurement Method Based on RGB Camera Using 1D Convolutional Neural Networks

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