Browsing by Author "Huang, Ren-You"

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A Motion-robust Contactless Photoplethysmography Using Chrominance and Adaptive Filtering
(2015-01-01)
Huang, Ren-You
;
Dung, Lan-Rong
;
電控工程研究所
;
Institute of Electrical and Control Engineering
Recently, the camera-based photoplethysmography (PPG) techniques have been proposed for heart rate measurement due to the contactless and low-cost features. However, the previous works showed limited success for moving subjects, especially when the signal of skin color changes rendered by vigorous motion is in the same frequency band as that by blood volume variations. This paper proposed a motion robust, contactless method for measuring heart rate when the human subject is exercising on some fitness machines, e.g. treadmill or stepper. The proposed method combines the chrominance-based method with adaptive filtering to make the heart rate measurement robust to vigorous motions on fitness machine while estimating the heart rate in frequency domain. To demonstrate the performance of heart rate measurement, we taped a video sequence of treadmill exercise with various motion themes. As shown in experimental results on the video sequence, our method can significantly improve the measurement accuracy from 61%
Measurement of heart rate variability using off-the-shelf smart phones
(2016-01-29)
Huang, Ren-You
;
Dung, Lan-Rong
;
電機工程學系
;
電控工程研究所
;
Department of Electrical and Computer Engineering
Background: The cardiac parameters, such as heart rate (HR) and heart rate variability (HRV), are very important physiological data for daily healthcare. Recently, the camera-based photoplethysmography techniques have been proposed for HR measurement. These techniques allow us to estimate the HR contactlessly with low-cost camera. However, the previous works showed limit success for estimating HRV because the R-R intervals, the primary data for HRV calculation, are sensitive to noise and artifacts. Methods: This paper proposed a non-contact method to extract the blood volume pulse signal using a chrominance-based method followed by a proposed CWT-based denoising technique. The R-R intervals can then be obtained by finding the peaks in the denoised signal. In this paper, we taped 12 video clips using the frontal camera of a smart phone with different scenarios to make comparisons among our method and the other alternatives using the absolute errors between the estimated HRV metrics and the ones obtained by an ECG-accurate chest band. Results: As shown in experiments, our algorithm can greatly reduce absolute errors of HRV metrics comparing with the related works using RGB color signals. The mean of absolute errors of HRV metrics from our method is only 3.53 ms for the static-subject video clips. Conclusions: The proposed camera-based method is able to produce reliable HRV metrics which are close to the ones measured by contact devices under different conditions. Thus, our method can be used for remote health monitoring in a convenient and comfortable way.