Modeling on the Feasibility of Camera-Based Blood Glucose Measurement

Wang, Yiyin; Wang, Wenjin; Gastel, Mark van; Haan, G. de

doi:10.1109/iccvw.2019.00212

Cited by 7 publications

(3 citation statements)

References 21 publications

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“…However, unlike oxygen saturation the variations in light measured via reflectance methods due to changes in glucose may be very difficult to detect. According to modeling by Wang et al [160] it is unlikely to detect the blood glucose based on either the DC or AC component of skin reflected light. Their model capture light in the visible to NIR range.…”

Section: Glucosementioning

confidence: 99%

Camera Measurement of Physiological Vital Signs

McDuff¹

2021

Preprint

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The need for remote tools for healthcare monitoring has never been more apparent. Camera measurement of vital signs leverages imaging devices to compute physiological changes by analyzing images of the human body. Building on advances in optics, machine learning, computer vision and medicine these techniques have progressed significantly since the invention of digital cameras. This paper presents a comprehensive survey of camera measurement of physiological vital signs, describing they vital signs that can be measured and the computational techniques for doing so. I cover both clinical and non-clinical applications and the challenges that need to be overcome for these applications to advance from proofs-of-concept. Finally, I describe the current resources (datasets and code) available to the research community and provide a comprehensive webpage (https://cameravitals.github.io/) with links to these resource and a categorized list of all the papers referenced in this article. CCS Concepts: • Human-centered computing → Ubiquitous and mobile computing; • Applied computing → Bioinformatics; • Computing methodologies → Computer vision.

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Section: Glucosementioning

confidence: 99%

Camera Measurement of Physiological Vital Signs

McDuff¹

2021

Preprint

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“…Pilt et al suggested diabetes patients may have higher pulse wave velocity than nondiabetic subjects. Researchers have proposed methods to detect glucose levels with a camera and spectrophotometer. , However, Wang et al suggested it is very challenging to measure blood glucose levels using a regular silicon-based camera with visible and NIR light sources. The reason is that the low-frequency glucose-induced intensity variations are very small, while the higher frequency components caused by glucose changes can be easily overwhelmed by SaO 2 changes due to the high absorption and HbO 2 concentration .…”

Section: Obtained Physiological Parameters and Disease Symptoms By Us...mentioning

confidence: 99%

Noncontact Physiological Measurement Using a Camera: A Technical Review and Future Directions

2020

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Using a camera as an optical sensor to monitor physiological parameters has garnered considerable research interest in biomedical engineering in recent decades. Researchers have explored the use of a camera for monitoring a variety of physiological waveforms, together with the vital signs carried by these waveforms. Most of the obtained waveforms are related to the human respiratory and cardiovascular systems, and in addition of being indicative of overall health, they can also detect early signs of certain diseases. While using a camera for noncontact physiological signal monitoring offers the advantages of low cost and operational ease, it also has the disadvantages such as vulnerability to motion and lack of burden-free calibration solutions in some use cases. This study presents an overview of the existing camera-based methods that have been reported in recent years. It introduces the physiological principles behind these methods, signal acquisition approaches, various types of acquired signals, data processing algorithms, and application scenarios of these methods. It also discusses the technological gaps between the camera-based methods and traditional medical techniques, which are mostly contact-based. Furthermore, we present the manner in which noncontact physiological signal monitoring use has been extended, particularly over the recent years, to more day-to-day aspects of individuals’ lives, so as to go beyond the more conventional use case scenarios. We also report on the development of novel approaches that facilitate easier measurement of less often monitored and recorded physiological signals. These have the potential of ushering a host of new medical and lifestyle applications. We hope this study can provide useful information to the researchers in the noncontact physiological signal measurement community.

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“…Despite resolving the need for using a sphygmomanometer, cuffless approaches still require wearable sensors and thus retain many challenges, such as cable or battery dependence, inability to use due to potential skin conditions like burns or sensitive skin of neonates, and general reluctance of users towards having to continuously carry wearables. Contact-free monitoring of people’s medical condition has thus seen great increase in popularity supported by state-of-the-art results for many physiological parameters, such as heart and respiratory rate (HR and RR) [ 9 ], oxygen saturation (SpO2) [ 10 ], glucose levels [ 11 ] and also BP [ 12 ]. Such contact-free physiological monitoring is most often based on remote photoplethysmography (rPPG), which uses the same underlying principle of changes in skin color due to blood volume changes as traditional PPG, but obtains the data remotely using a camera.…”

Section: Introductionmentioning

confidence: 99%

Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation

Slapničar,

Wang,

Luštrek

2024

Biomed. Opt. Express

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Single-site multi-wavelength (MW) pulse transit time (PTT) measurement was recently proposed using contact sensors with sequential illumination. It leverages different penetration depths of light to measure the traversal of a cardiac pulse between skin layers. This enabled continuous single-site MW blood pressure (BP) monitoring, but faces challenges like subtle skin compression, which importantly influences the PPG morphology and subsequent PTT. We extended this idea to contact-free camera-based sensing and identified the major challenge of color channel overlap, which causes the signals obtained from a consumer RGB camera to be a mixture of responses in different wavelengths, thus not allowing for meaningful PTT measurement. To address this, we propose novel camera-independent data-driven channel separation algorithms based on constrained genetic algorithms. We systematically validated the algorithms on camera recordings of palms and corresponding ground-truth BP measurements of 13 subjects in two different scenarios, rest and activity. We compared the proposed algorithms against established blind source separation methods and against previous camera-specific physics-based method, showing good performance in both PTT reconstruction and BP estimation using a Random Forest regressor. The best-performing algorithm achieved mean absolute errors (MAEs) of 3.48 and 2.61 mmHg for systolic and diastolic BP in a leave-one-subject-out experiment with personalization, solidifying the proposed algorithms as enablers of novel contact-free MW PTT and BP estimation.

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Modeling on the Feasibility of Camera-Based Blood Glucose Measurement

Cited by 7 publications

References 21 publications

Camera Measurement of Physiological Vital Signs

Camera Measurement of Physiological Vital Signs

Noncontact Physiological Measurement Using a Camera: A Technical Review and Future Directions

Generalized channel separation algorithms for accurate camera-based multi-wavelength PTT and BP estimation

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