Non-contact Heart Rate Monitoring for Intensive Exercise Based on Singular Spectrum Analysis

Xie, Kai; Fu, Chang-Hong; Liang, Huaiguo; Hong, Hong; Zhu, Xiaohua

doi:10.1109/mipr.2019.00048

Cited by 16 publications

(10 citation statements)

References 13 publications

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“…rPPG technology can be utilized to provide simple remote fitness tracking. In References [ 20 , 133 , 134 , 135 , 136 ], rPPG methods in fitness training settings were studied. In References [ 20 , 133 , 136 ], motion artifact during exercise was the major focus.…”

Section: Applicationsmentioning

confidence: 99%

“…In References [ 20 , 133 , 134 , 135 , 136 ], rPPG methods in fitness training settings were studied. In References [ 20 , 133 , 136 ], motion artifact during exercise was the major focus. In References [ 134 , 135 ], a feedback control system was implemented, as well, for adjusting the speed of the treadmill automatically.…”

Section: Applicationsmentioning

confidence: 99%

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Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Cheng

Wong

Chin

et al. 2021

Sensors

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Heart rate (HR) is one of the essential vital signs used to indicate the physiological health of the human body. While traditional HR monitors usually require contact with skin, remote photoplethysmography (rPPG) enables contactless HR monitoring by capturing subtle light changes of skin through a video camera. Given the vast potential of this technology in the future of digital healthcare, remote monitoring of physiological signals has gained significant traction in the research community. In recent years, the success of deep learning (DL) methods for image and video analysis has inspired researchers to apply such techniques to various parts of the remote physiological signal extraction pipeline. In this paper, we discuss several recent advances of DL-based methods specifically for remote HR measurement, categorizing them based on model architecture and application. We further detail relevant real-world applications of remote physiological monitoring and summarize various common resources used to accelerate related research progress. Lastly, we analyze the implications of research findings and discuss research gaps to guide future explorations.

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Section: Applicationsmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Cheng

Wong

Chin

et al. 2021

Sensors

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“…Wu et al introduced a time-frequency analysis method using continuous wavelet transform (CWT) [83] and a motion resistant spectral peak tracking (MRSPT) method [84] to measure HR via webcam, considering seven motion circumstances when subjects were driving, running and engaged in fitness training. Nevertheless, these methods considered very short ranges of around 1.5 m. For monitoring precise HR using a video camera, Xie et al [85] presented another method using singular spectrum analysis (SSA) where the motion signal measured from signal and SSA based on singular value decomposition (SVD) was employed to rectify motion artefacts during treadmill exercise. However, they only considered treadmill exercise and a small number of subjects.…”

Section: Motion Artefactsmentioning

confidence: 99%

“…Monitoring the physiological state using iPPG while doing exercise can be a convenient solution to overcome the adverse effects of over-exercising. In [74,78,79,82,85,93], iPPG was applied to monitor HR and RR during exercise, considering various fitness devices such as a stepping device, a treadmill, a bike, a hand bike, and a synchro-device.…”

Section: Fitness Monitoringmentioning

confidence: 99%

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

2019

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Techniques for noncontact measurement of vital signs using camera imaging technologies have been attracting increasing attention. For noncontact physiological assessments, computer vision-based methods appear to be an advantageous approach that could be robust, hygienic, reliable, safe, cost effective and suitable for long distance and long-term monitoring. In addition, video techniques allow measurements from multiple individuals opportunistically and simultaneously in groups. This paper aims to explore the progress of the technology from controlled clinical scenarios with fixed monitoring installations and controlled lighting, towards uncontrolled environments, crowds and moving sensor platforms. We focus on the diversity of applications and scenarios being studied in this topic. From this review it emerges that automatic multiple regions of interest (ROIs) selection, removal of noise artefacts caused by both illumination variations and motion artefacts, simultaneous multiple person monitoring, long distance detection, multi-camera fusion and accepted publicly available datasets are topics that still require research to enable the technology to mature into many real-world applications.

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“… 24 . Moreover, in some papers, 25 HR was measured by using the

, which was the intersection of the projection planes used in Refs. 24 and 26 .…”

Section: Introductionmentioning

confidence: 99%

Research on the combination of color channels in heart rate measurement based on photoplethysmography imaging

et al. 2021

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. Significance: The measurement of human vital signs based on photoplethysmography imaging (PPGI) can be severely affected by the interference of various factors in the measurement process; therefore, a lot of complex signal processing techniques are used to remove the influence of the interference. Aim: We comprehensively analyze several methods for color channel combination in the color spaces currently used in PPGI and determine the combination method that can improve the quality of the pulse signal, which results in a modified plane-orthogonal-to-skin based method (POS). Approach: Based on the analysis of the previous studies, 13 methods for color channel combination in the different color spaces, which can be seen as having potential abilities in measuring vital signs, were compared by employing the average value of signal-to-noise ratio (SNR) and the box-plot in the public databases UBFC-RPPG and PURE. In addition, the pulse signal was extracted through the dual-color space transformation (sRGB → intensity normalized RGB → YCbCr) and fine-tuning on the CbCr plane. Results: Among the 13 methods for color channel combination, the signal extracted by the Cb+Cr combination in the YCbCr color space includes the most pulse information. Furthermore, the average SNR of the modified POS for all the used databases is improved by 69.3% compared to POS. Conclusions: The methods using prior knowledge are not only simple to calculate but can significantly increase the SNR, which will provide a great help in the practical use of vital sign measurements based on PPGI.

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Non-contact Heart Rate Monitoring for Intensive Exercise Based on Singular Spectrum Analysis

Cited by 16 publications

References 13 publications

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Deep Learning Methods for Remote Heart Rate Measurement: A Review and Future Research Agenda

Remote Monitoring of Vital Signs in Diverse Non-Clinical and Clinical Scenarios Using Computer Vision Systems: A Review

Research on the combination of color channels in heart rate measurement based on photoplethysmography imaging

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