摘要 :
The prevalence of cardiovascular diseases in China is still on the rise, and it is estimated that 330 million people are suffering from cardiovascular diseases. In terms of physical health and mental health, a heart rate detection...
展开
The prevalence of cardiovascular diseases in China is still on the rise, and it is estimated that 330 million people are suffering from cardiovascular diseases. In terms of physical health and mental health, a heart rate detection technology that is portable can be measured at any time, safe, comfortable, simple to operate, and low-cost is essential. Given the inevitable jitter of handheld mobile phones, based on the accuracy of remote photoplethysmography (rPPG) detection technology, we propose a moving window timing sampling to refine the original video frame signal. The heart rate value can be extracted by processing such as region of interest (ROI) selection, high-pass filtering, blind source separation algorithm, Fourier transforms, and peak detection. Compared with the heart rate value obtained without using the moving window timing sampling, we found that the effect of the moving window timing is about 10 seconds is the best. The root means as the square error (RMSE), mean absolute error (MAE), and standard deviation (STD) are the lowest, 6.6929, 5.1365, 6.6165 respectively. The errors compared to the sampling without moving piecewise function are 13.53, 10.79, 14.09, The errors were reduced by 50.5%, 52.4%, and 53.04% respectively.
收起
摘要 :
Remote photoplethysmography (rPPG) is a non-contact technique for measuring vital physiological signs, such as heart rate (HR) and respiratory rate (RR). HR is a medical index which is widely used in health monitoring and emotion ...
展开
Remote photoplethysmography (rPPG) is a non-contact technique for measuring vital physiological signs, such as heart rate (HR) and respiratory rate (RR). HR is a medical index which is widely used in health monitoring and emotion detection applications. Therefore, HR measurement with rPPG methods offers a convenient and non-invasive method for these applications. The selection of Region Of Interest (ROI) is a critical first step of many rPPG techniques to obtain reliable pulse signals. The ROI should contain as many skin pixels as possible with a minimum of non-skin pixels. Moreover, it has been shown that rPPG signal is not distributed homogeneously on skin. Some skin regions contain more rPPG signal than others, mainly for physiological reasons. In this paper, we propose to explicitly favor areas where the information is more predominant using a spatially weighted average of skin pixels based on a trained model. The proposed method has been compared to several state of the art ROI segmentation methods using a public database, namely the UBFC-RPPG dataset (Bobbia et al., 2017). We have shown that this modification in how the spatial averaging of the ROI pixels is calculated can significantly increase the final performance of heart rate estimate.
收起
摘要 :
Recently, remote photoplethysmography (rPPG) has been studied and developed not only in a controlled environment but also in a wild environment such as telemedicine and driver monitoring. Although photoplethysmography (PPG) can be...
展开
Recently, remote photoplethysmography (rPPG) has been studied and developed not only in a controlled environment but also in a wild environment such as telemedicine and driver monitoring. Although photoplethysmography (PPG) can be measured through a contact sensor, pulse signal can be obtained by remote measuring minute color changes on the skin surface using a camera. This pulse signal is called rPPG and has an advantage of sensing cardiac activity without a contact sensor. The processing pipeline of rPPG can be simply defined as region of interest selection (ROI), pulse signal extraction, signal processing. During this process, in ROI selection, skin segmentation is performed because only the skin pixel region is related to the rPPG signal. We propose extremely lightweight skin segmentation network (ELSNet) for applying deep learning to skin segmentation to measure reliable signals. Our method improved the success rate within 5BPM of heart rate estimation by about 6%, and in the talking environment, an average performance improvement of 9.5% was confirmed. In addition, it was confirmed that MAPE was improved by an average of 20%. The ELSNet shows 167 FPS throughput on Intel i9 CPU.
收起
摘要 :
Remote photoplethysmography (rPPG) has attracted much attention in recent years. This research proposes to apply rPPG to the fitness training scenario, enabling non-contact measurement of the subject's heart rate during training. ...
展开
Remote photoplethysmography (rPPG) has attracted much attention in recent years. This research proposes to apply rPPG to the fitness training scenario, enabling non-contact measurement of the subject's heart rate during training. Currently, most existing approaches suffer from a major weakness, i.e. the subject's body needs to remain stably while conducting measurement, which significantly hinders practical applications of the approach. The main purpose of this paper is to build a training system based on rPPG and fitness machines to provide users with better ergonomic exercise experiences. We have built a spinning bike system that combines a camera and an adaptive controller based on heart rate feedback for tracking the desired exercise intensity. Fuzzy control is introduced in the feedback control loop by considering heart rate and heart rate variability simultaneously for better representation of the physical status. Some preliminary results are briefly presented. This research demonstrates promising performance improvement by combining rPPG heart rate estimation and fitness machine control.
收起
摘要 :
Remote photoplethysmography (rPPG) has attracted much attention in recent years. This research proposes to apply rPPG to the fitness training scenario, enabling non-contact measurement of the subject's heart rate during training. ...
展开
Remote photoplethysmography (rPPG) has attracted much attention in recent years. This research proposes to apply rPPG to the fitness training scenario, enabling non-contact measurement of the subject's heart rate during training. Currently, most existing approaches suffer from a major weakness, i.e. the subject's body needs to remain stably while conducting measurement, which significantly hinders practical applications of the approach. The main purpose of this paper is to build a training system based on rPPG and fitness machines to provide users with better ergonomic exercise experiences. We have built a spinning bike system that combines a camera and an adaptive controller based on heart rate feedback for tracking the desired exercise intensity. Fuzzy control is introduced in the feedback control loop by considering heart rate and heart rate variability simultaneously for better representation of the physical status. Some preliminary results are briefly presented. This research demonstrates promising performance improvement by combining rPPG heart rate estimation and fitness machine control.
收起
摘要 :
Remote photoplethysmography (rPPG) has attracted much attention in recent years. This research proposes to apply rPPG to the fitness training scenario, enabling non-contact measurement of the subject's heart rate during training. ...
展开
Remote photoplethysmography (rPPG) has attracted much attention in recent years. This research proposes to apply rPPG to the fitness training scenario, enabling non-contact measurement of the subject's heart rate during training. Currently, most existing approaches suffer from a major weakness, i.e. the subject's body needs to remain stably while conducting measurement, which significantly hinders practical applications of the approach. The main purpose of this paper is to build a training system based on rPPG and fitness machines to provide users with better ergonomic exercise experiences. We have built a spinning bike system that combines a camera and an adaptive controller based on heart rate feedback for tracking the desired exercise intensity. Fuzzy control is introduced in the feedback control loop by considering heart rate and heart rate variability simultaneously for better representation of the physical status. Some preliminary results are briefly presented. This research demonstrates promising performance improvement by combining rPPG heart rate estimation and fitness machine control.
收起
摘要 :
Conventional contact photoplethysmography (PPG) sensors are not suitable in situations of skin damage or when unconstrained movement is required. As a consequence, remote photoplethysmography (rPPG) has recently emerged because it...
展开
Conventional contact photoplethysmography (PPG) sensors are not suitable in situations of skin damage or when unconstrained movement is required. As a consequence, remote photoplethysmography (rPPG) has recently emerged because it provides remote physiological measurements without expensive hardware and improves comfort for longterm monitoring. RPPG estimation methods use the spatially averaged RGB values of pixels in a Region Of Interest (ROI) to generate a temporal RGB signal. The selection of ROI is a critical first step to obtain reliable pulse signals and must contain as many skin pixels as possible with a low percentage of non-skin pixels. In this paper, we experimentally compare seven ROI segmentation methods in the perspective of heart rate (HR) measurements with dedicated metrics. The algorithms are compared using our in-house database UBFC-RPPG, comprising of 53 videos specifically geared towards rPPG analysis.
收起
摘要 :
Measuring bio signals such as the heart rate in non medical applications is gaining an increasing importance. With camera based photoplethysmography (PPG) it is possible to measure the heart rate remotely with built in webcams of ...
展开
Measuring bio signals such as the heart rate in non medical applications is gaining an increasing importance. With camera based photoplethysmography (PPG) it is possible to measure the heart rate remotely with built in webcams of every tablet and laptop. Recent research with machine learning based methods showed great success compared to signal processing based methods. In this paper, we use k-nearest neighbor (kNN) and multilayer perceptron (MLP) with an alternative representation of the input vector. Estimating the quality of peaks with a Gaussian distribution could further improve the detection. Overall we could improve the root mean square error (RMSE) from 23.97 to 8.62.
收起
摘要 :
Measuring bio signals such as the heart rate in non medical applications is gaining an increasing importance. With camera based photoplethysmography (PPG) it is possible to measure the heart rate remotely with built in webcams of ...
展开
Measuring bio signals such as the heart rate in non medical applications is gaining an increasing importance. With camera based photoplethysmography (PPG) it is possible to measure the heart rate remotely with built in webcams of every tablet and laptop. Recent research with machine learning based methods showed great success compared to signal processing based methods. In this paper, we use k-nearest neighbor (kNN) and multilayer perceptron (MLP) with an alternative representation of the input vector. Estimating the quality of peaks with a Gaussian distribution could further improve the detection. Overall we could improve the root mean square error (RMSE) from 23.97 to 8.62.
收起
摘要 :
Nowadays, healthcare is becoming a new issue increasingly that worthy to care. Healthcare measurement methods can be categorized into two main areas: contact and contactless. Among them, contactless has more flexibility and comfor...
展开
Nowadays, healthcare is becoming a new issue increasingly that worthy to care. Healthcare measurement methods can be categorized into two main areas: contact and contactless. Among them, contactless has more flexibility and comfort. In this paper, we combined the pulse rate measurement and face recognition based on deep learning into an embedded system. The system can help people measure their pulse rate in real-time after recognize the face. Our system contains a graphics processing unit (GPU) and central processing unit (CPU) co-design platform in order to processing face recognition in real-time. The experimental results show the recognition average accuracy is 98%. In pulse rate detection aspect, the provided mean absolute error (MAE) and root-mean-square error (RMSE) are 5.095 BPM and 5.976 BPM. The results reveals that our system can truly apply in daily life. In the future, we hope our system can be utilized in several scenarios such as company door-access, driving monitor, industry, etc.
收起
原文获取