Spatio-temporal filtering of thermal video sequences for heart rate estimation

Hamedani, Kian; Bahmani, Zahra; Mohammadian, Amin

doi:10.1016/j.eswa.2016.01.022

Cited by 19 publications

(12 citation statements)

References 25 publications

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“…Thermal video in conjunction with EVM has been shown to identify respiration rates and respiration patterns indicative of central and obstructive sleep apnea, and has been used to measure arterial pulse at the feet [19], [21]. The EVM method has also been applied to thermal video of an adult's face to reveal heart rate [20], [68].…”

Section: Eulerian Video Magnificationmentioning

confidence: 99%

“…The heat transfer properties of blood vessels and skin, individually and combined, are not a new topic and have been modeled using the Pennes' bioheat model [74]- [76]. While this interaction is complex, a simple result is that the skin can change temperature periodically as a result of perfusion; blood at core body temperature is pumped to surface veins at a temperature closer to room temperature [68]. These temperature changes are within the mid-wave thermal camera temperature capture capabilities [43].…”

Section: Pulse Measurementsmentioning

confidence: 99%

“…In this thesis, proximal measurements were extracted from the head, neck and chest. Studies have shown that pulse measurements can be extracted from these body parts with great accuracy [68], [43]. Distal measurements were extracted from subjects' feet, a region not currently extensively examined.…”

Section: Figure 52 Blood Simulation Model Roimentioning

confidence: 99%

“…This chapter examines the best region to extract pulse measurements from. Binary masks were used to create regions of interest [68], and Buddharaju et. al.…”

Section: Figure 52 Blood Simulation Model Roimentioning

confidence: 99%

“…Application of video-temporal processing such as EVM (and AEVM) to thermal video data has only been examined more recently [68], [19], [18]. As a recent body of work, and with several parameters affecting system outputs, the developed algorithms require robust testing to identify specific patterns and behaviors, specifically in non-ideal conditions.…”

Section: Video Processing Parameter Testingmentioning

confidence: 99%

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Non-Contact Bed-Based Monitoring of Vital Signs

Bennett¹

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Vital signs are a set of commonly measured signals used internationally as a baseline in medicine and surgery and are one of the most accurate predictors of clinical and physiological deterioration. Despite the clear clinical importance of vital sign measurements, there is often missed or inadequate documentation of patient vital sign measurements. The development of unobtrusive, automated and continuous monitoring offers the potential to enhance the safety and quality of patient care. This thesis details a system that uses multiple modalities to capture data and data processing techniques to extract vital signal measurements and vital signal measurement abnormalities related to subject morbidities. This thesis focuses on the examination, testing and improvement upon a current visible light video processing technique intended to extract vital signal measurements, and expand it's use to thermal infrared video vital signal extraction. Three modalities were used to gather data from healthy adult subjects and older adult inhospital patients: thermal infrared cameras, visible light cameras and pressure sensitive mats. Subjects participated in several experimental procedures including video data capture of faces, hands and feet as well as in-bed pressure mat data capture. This data was subjected to several stages of data processing to extract vital signal measurements, which include pulse, respiration temperature and mobility measurements. Data segmentation using binary masks, level set method, and watershed method were used to identify regions of interest. An adaptive spatio-temporal video processing algorithm, the main thesis contribution, was used to extract vital signal measurements. The developed algorithm was assessed for its performance in vital signal estimation, as well its

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Section: Eulerian Video Magnificationmentioning

confidence: 99%

Section: Pulse Measurementsmentioning

confidence: 99%

Section: Figure 52 Blood Simulation Model Roimentioning

confidence: 99%

“…This chapter examines the best region to extract pulse measurements from. Binary masks were used to create regions of interest [68], and Buddharaju et. al.…”

Section: Figure 52 Blood Simulation Model Roimentioning

confidence: 99%

Section: Video Processing Parameter Testingmentioning

confidence: 99%

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