- 标题
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- 实验方案
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A Stochastic Gradient Approach for Robust Heartbeat Detection with Doppler Radar Using Time-Window-Variation Technique
摘要： Heart rate variability (HRV) indicates health condition and mental stress. The development of non-contact heart rate (HR) monitoring technique with Doppler radar is attracting great attentions. However, the performance of heartbeat detection via radar signal easily degrades, due to respiration and body motion. In this paper, first, a stochastic gradient approach is applied to reconstruct high-resolution spectrum of heartbeat, by proposing the zero-attracting sign least-mean-square (ZA-SLMS) algorithm. To correct the quantized gradient of cost function, and penalize the sparse constraint on the updating spectrum, more accurate heartbeat spectrum is reconstructed. Then, to better adapt to the noises with different strengths caused by subjects’ movements, an adaptive regularization parameter (AREPA) is introduced in the ZA-SLMS algorithm as an improved variant, which can adaptively regulate the proportion between gradient correction and sparse penalty. Moreover, in view of the stability of location of spectral peak associated with HR when the size of time window slightly changes, a time-window-variation (TWV) technique is further incorporated in the improved ZA-SLMS (IZA-SLMS) algorithm, for more stable HR estimation. Through the experiments on five subjects, our proposal is demonstrated to bring a significant improvement of accuracy against existing detection methods. Specifically, the IZA-SLMS algorithm with TWV achieves the smallest average error of 3.79 beats per minute (BPM), when subjects type with a laptop.
关键词： Doppler radar,Non-contact heartbeat detection,sparse spectrum reconstruction (SSR),adaptive filter,time-window-variation (TWV),heart rate (HR)
更新于2025-09-09 09:28:46
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[IEEE 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) - Tirunelveli, India (2018.5.11-2018.5.12)] 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI) - An Efficient Iris Recognition System Using Integer Wavelet Transform
摘要： An iris recognition mechanism is being discussed in presented paper, which is based on Integer Wavelet Transform (IWT). UBIRIS.v2 iris database is works as the input image for implementation of this mechanism. For ef?cient iris region localization combination of Total Variation Model and Hough Transform is used. An iris recognition algorithm is developed by using four level integer wavelet transform (IWT). IWT is an interesting alternative of discrete wavelet transform, which gives less computational complexity because it gives integer value as the coef?cient. Four level IWT is ?rstly applying into the segmented iris image, which generates 256 sub-bands. But only 192 sub-images are taken into consideration after ignoring the high frequency sub-bands. After that the energy of 192 sub-bands is calculated for feature extraction, which helps to generate a 192 bit binary code. For recognition of individuals, normalized hamming distance is computed between the trained and input binary codes of 1-D feature vector. Matching results have been plotted and discussed in the paper.
关键词： SSR (Single Scale Retinex),Hough Transform,IWT
更新于2025-09-09 09:28:46
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[IEEE 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Bangalore (2018.2.9-2018.2.10)] 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC) - Determination of Absolute Heart Beat from Photoplethysmographic Signals in the Presence of Motion Artifacts
摘要： In Wireless Body Area Networks (WBANs), accurate monitoring of heart rate (HR) using Photoplethysmography (PPG) signals is always a difficult task, especially when the subjects are under radical exercises. This is due to the signals corrupted by severely strong Motion Artifacts (MA) caused by the subject’s body movements. In this work, a novel approach has been proposed consisting of signal decomposition for denoising using principal component analysis (PCA), spare signal reconstruction (SSR), peak detection and tracking and support vector machine (SVM) classifier for accurate estimation of HR, based on the wrist type PPG signals. With this approach, we are able to achieve high accuracy and also, it is strong enough to remove MA. Experiments were conducted on 12 subjects and their datasets are obtained from 2015 IEEE Signal Processing CUP, running on a threadmill with varying speeds ranging from 0 to a maximum speed of 15 km/hour. From the results, it is observed that the average absolute error of heart rate estimation is 1.66 beats per minute (BPM).
关键词： SVM classifier,PCA,HR,Wireless Body Area Networks (BAN),SSR,Accelerometer,PPG
更新于2025-09-04 15:30:14