scholarly journals Noncontact Heart Rate Measurement Using a Webcam, Based on Joint Blind Source Separation and a Skin Reflection Model: For a Wide Range of Imaging Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Boyuan Zhang ◽  
Hengkang Li ◽  
Lisheng Xu ◽  
Lin Qi ◽  
Yudong Yao ◽  
...  
Keyword(s):  
Heart Rate ◽  
Blind Source Separation ◽  
Body Movement ◽  
Source Separation ◽  
New Approach ◽  
Factors Affecting ◽  
Reflection Model ◽  
Wide Range ◽  
Rate Information ◽  
Imaging Conditions

Remote photoplethysmography (rPPG) can be used for noncontact and continuous measurement of the heart rate (HR). Currently, the main factors affecting the accuracy and robustness of rPPG-based HR measurement methods are the subject’s skin tone, body movement, exercise recovery, and variable or inadequate illumination. In response to these challenges, this study is aimed at investigating a rPPG-based HR measurement method that is effective under a wide range of conditions by only using a webcam. We propose a new approach, which combines joint blind source separation (JBSS) and a projection process based on a skin reflection model, so as to eliminate the interference of background illumination and enhance the extraction of pulse rate information. Three datasets derived from subjects with different skin tones considering six environmental scenarios are used to validate the proposed method against three other state-of-the-art methods. The results show that the proposed method can provide more accurate and robust HR measurement for all three datasets and is therefore more applicable to a wide range of scenarios.

Factors Affecting Molecular Self-Assembly and Its Mechanism

2012 ◽  
Vol 9 (1) ◽  
pp. 43 ◽  
Author(s):  
Hueyling Tan
Keyword(s):  
Self Assembly ◽  
Building Blocks ◽  
Molecular Engineering ◽  
Molecular Structures ◽  
Polymer Science ◽  
New Approach ◽  
Factors Affecting ◽  
Dna Structures ◽  
Self Assembling ◽  
Wide Range

Molecular self-assembly is ubiquitous in nature and has emerged as a new approach to produce new materials in chemistry, engineering, nanotechnology, polymer science and materials. Molecular self-assembly has been attracting increasing interest from the scientific community in recent years due to its importance in understanding biology and a variety of diseases at the molecular level. In the last few years, considerable advances have been made in the use ofpeptides as building blocks to produce biological materials for wide range of applications, including fabricating novel supra-molecular structures and scaffolding for tissue repair. The study ofbiological self-assembly systems represents a significant advancement in molecular engineering and is a rapidly growing scientific and engineering field that crosses the boundaries ofexisting disciplines. Many self-assembling systems are rangefrom bi- andtri-block copolymers to DNA structures as well as simple and complex proteins andpeptides. The ultimate goal is to harness molecular self-assembly such that design andcontrol ofbottom-up processes is achieved thereby enabling exploitation of structures developed at the meso- and macro-scopic scale for the purposes oflife and non-life science applications. Such aspirations can be achievedthrough understanding thefundamental principles behind the selforganisation and self-synthesis processes exhibited by biological systems.

scholarly journals Blind Source Separation for the Aggregation of Machine Learning Algorithms: An Arrhythmia Classification Case

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 425
Author(s):  
Krzysztof Gajowniczek ◽  
Iga Grzegorczyk ◽  
Michał Gostkowski ◽  
Tomasz Ząbkowski
Keyword(s):  
Blind Source Separation ◽  
Cardiac Arrhythmias ◽  
Classification Accuracy ◽  
Recursive Partitioning ◽  
Source Separation ◽  
Classification Problem ◽  
Machine Learning Algorithms ◽  
Physiological Signals ◽  
New Approach ◽  
Model Aggregation

In this work, we present an application of the blind source separation (BSS) algorithm to reduce false arrhythmia alarms and to improve the classification accuracy of artificial neural networks (ANNs). The research was focused on a new approach for model aggregation to deal with arrhythmia types that are difficult to predict. The data for analysis consisted of five-minute-long physiological signals (ECG, BP, and PLETH) registered for patients with cardiac arrhythmias. For each patient, the arrhythmia alarm occurred at the end of the signal. The data present a classification problem of whether the alarm is a true one—requiring attention or is false—should not have been generated. It was confirmed that BSS ANNs are able to detect four arrhythmias—asystole, ventricular tachycardia, ventricular fibrillation, and tachycardia—with higher classification accuracy than the benchmarking models, including the ANN, random forest, and recursive partitioning and regression trees. The overall challenge scores were between 63.2 and 90.7.

scholarly journals A New Criterion for Bounded Component Analysis

2021 ◽  
Author(s):  
Renan Brotto ◽  
Kenji Nose-Filho ◽  
João M. T. Romano
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Component Analysis ◽  
Rotation Matrix ◽  
Separation Problem ◽  
Correlated Sources ◽  
New Approach ◽  
Bounded Component ◽  
New Criterion

<div>In this paper we present a new criterion for bounded component analysis, a quite new approach for the Blind Source Separation problem. For the determined case, we show that the `1-norm of the estimated sources can be used as a contrast for the problem. We present a blind algorithm for the source separation of independents sources or mixtures of correlated sources by only a rotation matrix. We also present a variety of simulations assessing the performance of the proposed approach.</div>

Augmented Hardware Design of BSS in Real Time by Real Time Feasible Source Signal Separator

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Source Separation ◽  
Separation Algorithm ◽  
Underdetermined Blind Source Separation ◽  
Memory Space ◽  
The Real ◽  
Wide Range ◽  
Source Signal ◽  
Band Limited

Blind source separation is a blooming sector in the digital signal processing for severing exact signal from the dense recorded. Exclusively, among the “Blind Source Separation” the “Under Determined Blind Source Separation” is considered than an over determined Blind Source Separation due to its wide range of usage. Nevertheless, it is seen that the real implementation is very rarely done in existing researches, because the real time implementation of UBSS (Underdetermined Blind Source Separation)is existed to be a challenging one due to its lacking hardware characteristics of increased latency, reduced speed and consumption of more memory space. Consequently, there is an increase need to implement an Underdetermined source signal separation real time with improved hardware utility that in this Unswerving framework a Real time feasible Source Signal separator is formulated in which initially the source signals are decomposed by Boosted band-limited VMD (Variational Mode Decomposition)into the “Multi component Signal” and then to an amount of "Band-Limited” IMF subjected to the Encompassed Hammer sley–Clifford source separation algorithm that uses expectation-maximization and Gibbs sampling an alternative to deterministic algorithms to determine the exact estimated parameter from E-M method. Subsequently, the source separation algorithm infers the best separation of sources signals by exact estimation and determination from the decomposed signals, whereas the iterations in E-M estimation are reduced by Gauss-Seidel method. Thus our novel source signal separator internally with a signal decomposer and a source separation algorithm with lesser number of iterations which reduces memory consumption and yields better hardware realization with reduced latency and increased speed. The proposed Implementation is done in Xilinx Platform.

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