Heart rate measurement based on a time-lapse image

Takano, Chihiro; Ohta, Yoshihiro

doi:10.1016/j.medengphy.2006.09.006

Cited by 304 publications

(207 citation statements)

References 32 publications

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“…In textured regions like the hand, more feature corners can be detected than the plain regions (e.g., background wall); and (2) shows that the whole image is segmented into multiple triangular subregions, in which some are skin regions (e.g., subregion A) that contain pulse-signals while others are background scenes (e.g., subregion B, C and D). The following steps aim to distinguish the skin regions from non-skin regions (background) by feature extraction and clustering.…”

Section: Subregion Generationmentioning

confidence: 99%

See 1 more Smart Citation

Automatic RoI Detection for Camera-Based Pulse-Rate Measurement

Luijtelaar¹,

Wang

Stuijk

et al. 2015

Lecture Notes in Computer Science

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Abstract. Remote photoplethysmography (rPPG) enables contactless measurement of pulse-rate by detecting pulse-induced colour changes on human skin using a regular camera. Most of existing rPPG methods exploit the subject face as the Region of Interest (RoI) for pulse-rate measurement by automatic face detection. However, face detection is a suboptimal solution since (1) not all the subregions in a face contain the skin pixels where pulse-signal can be extracted, (2) it fails to locate the RoI in cases when the frontal face is invisible (e.g., side-view faces). In this paper, we present a novel automatic RoI detection method for camera-based pulse-rate measurement, which consists of three main steps: subregion tracking, feature extraction, and clustering of skin regions. To evaluate the robustness of the proposed method, 36 video recordings are made of 6 subjects with different skin-types performing 6 types of head motion. Experimental results show that for the video sequences containing subjects with brighter skin-types and modest body motions, the accuracy of the pulse-rates measured by our method (94%) is comparable to that obtained by a face detector (92%), while the average SNR is significantly improved from 5.8 dB to 8.6 dB.

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Section: Subregion Generationmentioning

confidence: 99%

“…In contrast, non-contact based vital signs monitoring is preferred for its unobtrusiveness and noninvasiveness. A promising alternative is the recently introduced camera-based pulse-rate monitoring -remote photoplethysmography (rPPG) [2,3].…”

Section: Introductionmentioning

confidence: 99%

Automatic RoI Detection for Camera-Based Pulse-Rate Measurement

Luijtelaar¹,

Wang

Stuijk

et al. 2015

Lecture Notes in Computer Science

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show abstract

“…Takano et al first utilized the trace of skin color changes in facial video to extract heartbeat signal and estimate HR [3]. They recorded the variations in the average brightness of the Region of Interest (ROI) -a rectangular area on the subject's cheekto estimate HR.…”

Section: Introductionmentioning

confidence: 99%

“…iv. Most of the facial video-based fully automatic HR estimation methods, including color-based [3]- [5], [19] and motion-based [12], assume that the head is static (or close to) during data capture. This means that there is neither internal facial motion nor external movement or rotation of the head during the data acquisition phase.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Heartbeat Peak Locations and Heartbeat Rate from Facial Video

2017

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“…This periodic change of facial color is associated with the periodic heartbeat signal and can be traced in a facial video. Takano et al first utilized this fact in order to generate heartbeat signal from a facial video and, in turns, calculated Heartbeat Rate (HR) from that signal [19]. A number of other methods also utilized heartbeat signal obtained from facial video for measuring different physiological parameters such as HR [20], respiratory rate and blood pressure [21], and muscle fatigue [20].…”

Section: Introductionmentioning

confidence: 99%