Camera-based heart rate monitoring in highly dynamic light conditions

Jeanne, Vincent; Asselman, Michel; Brinker, Bert den; Bulut, Murtaza

doi:10.1109/iccve.2013.6799899

Cited by 20 publications

(11 citation statements)

References 6 publications

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“…Hence, a correction algorithm is required. For example, Jeanne et al [ 187 ] applied spatio-temporal filtering and achieved a correlation with the reference of 0.99. Lee et al [ 188 ] used knowledge about the external light source to correct the camera video.…”

Section: Challenges Of In-vehicle Measurementsmentioning

confidence: 99%

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Leonhardt

Leicht

Teichmann

2018

Sensors

103

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This review provides an overview of unobtrusive monitoring techniques that could be used to monitor some of the human vital signs (i.e., heart activity, breathing activity, temperature and potentially oxygen saturation) in a car seat. It will be shown that many techniques actually measure mechanical displacement, either on the body surface and/or inside the body. However, there are also techniques like capacitive electrocardiogram or bioimpedance that reflect electrical activity or passive electrical properties or thermal properties (infrared thermography). In addition, photopleythysmographic methods depend on optical properties (like scattering and absorption) of biological tissues and—mainly—blood. As all unobtrusive sensing modalities are always fragile and at risk of being contaminated by disturbances (like motion, rapidly changing environmental conditions, triboelectricity), the scope of the paper includes a survey on redundant sensor arrangements. Finally, this review also provides an overview of automotive demonstrators for vital sign monitoring.

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Section: Challenges Of In-vehicle Measurementsmentioning

confidence: 99%

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Leonhardt

Leicht

Teichmann

2018

Sensors

103

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“…In [176], the authors proposed an artifact reduction method, which is caused by lighting variation. Another option is to use an IR-based camera system suitable for automotive applications [177]. …”

Section: Cognitive Distractionmentioning

confidence: 99%

Driver Distraction Using Visual-Based Sensors and Algorithms

Fernández¹,

Usamentiaga

Carús³

et al. 2016

Sensors

101

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Driver distraction, defined as the diversion of attention away from activities critical for safe driving toward a competing activity, is increasingly recognized as a significant source of injuries and fatalities on the roadway. Additionally, the trend towards increasing the use of in-vehicle information systems is critical because they induce visual, biomechanical and cognitive distraction and may affect driving performance in qualitatively different ways. Non-intrusive methods are strongly preferred for monitoring distraction, and vision-based systems have appeared to be attractive for both drivers and researchers. Biomechanical, visual and cognitive distractions are the most commonly detected types in video-based algorithms. Many distraction detection systems only use a single visual cue and therefore, they may be easily disturbed when occlusion or illumination changes appear. Moreover, the combination of these visual cues is a key and challenging aspect in the development of robust distraction detection systems. These visual cues can be extracted mainly by using face monitoring systems but they should be completed with more visual cues (e.g., hands or body information) or even, distraction detection from specific actions (e.g., phone usage). Additionally, these algorithms should be included in an embedded device or system inside a car. This is not a trivial task and several requirements must be taken into account: reliability, real-time performance, low cost, small size, low power consumption, flexibility and short time-to-market. The key points for the development and implementation of sensors to carry out the detection of distraction will also be reviewed. This paper shows a review of the role of computer vision technology applied to the development of monitoring systems to detect distraction. Some key points considered as both future work and challenges ahead yet to be solved will also be addressed.

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“…The use of regular RGB cameras was challenged by Jeanne et al [4]. They explored the possibility of touchless monitoring the heart rate for automotive applications and found that in highly dynamic light conditions infra-red (IR)-based detection (in a range of 700 nm to 1000 nm) is much more robust.…”

Section: Related Workmentioning

confidence: 99%

Distant Measurement of Plethysmographic Signal in Various Lighting Conditions Using Configurable Frame-Rate Camera

Przybyło

Kańtoch

Jabłoński

et al. 2016

Metrology and Measurement Systems

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Videoplethysmography is currently recognized as a promising noninvasive heart rate measurement method advantageous for ubiquitous monitoring of humans in natural living conditions. Although the method is considered for application in several areas including telemedicine, sports and assisted living, its dependence on lighting conditions and camera performance is still not investigated enough. In this paper we report on research of various image acquisition aspects including the lighting spectrum, frame rate and compression. In the experimental part, we recorded five video sequences in various lighting conditions (fluorescent artificial light, dim daylight, infrared light, incandescent light bulb) using a programmable frame rate camera and a pulse oximeter as the reference. For a video sequence-based heart rate measurement we implemented a pulse detection algorithm based on the power spectral density, estimated using Welch's technique. The results showed that lighting conditions and selected video camera settings including compression and the sampling frequency influence the heart rate detection accuracy. The average heart rate error also varies from 0.35 beats per minute (bpm) for fluorescent light to 6.6 bpm for dim daylight.

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Camera-based heart rate monitoring in highly dynamic light conditions

Cited by 20 publications

References 6 publications

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Unobtrusive Vital Sign Monitoring in Automotive Environments—A Review

Driver Distraction Using Visual-Based Sensors and Algorithms

Distant Measurement of Plethysmographic Signal in Various Lighting Conditions Using Configurable Frame-Rate Camera

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