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1

Video-based HR measurement using adaptive facial regions with multiple color spaces

Panigrahi Arpita, Sharma Hemant, Mukherjee Atin

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 1

68--82

EN

Driven by the desire for feasible and convenient healthcare, non-contact heart rate (HR) monitoring based on consumer-grade cameras has gained significant recognition among researchers. However, this technology suffers from performance reliability and consistency in realistic situations of motion artifacts, illumination variations, and skin tones, limiting it to emerge as an alternative to conventional methods. Considering these challenges, this paper suggests an effective technique for HR measurement from facial RGB videos. The face being the region of interest (ROI) is divided into several small sub-ROIs of even size. A group of quality sub-ROIs is formed and weighted based on the fundamental periodicity coefficient to handle spatial non-uniform illumination and facial motions. Five different color spaces are considered, and the most suitable color component from each space is chosen to alleviate the influence of temporal illumination variation and other factors. The resultant color signals are denoised using the ensemble empirical mode decomposition and integrated using the principal component analysis to derive a pulsating component representing the blood volumetric changes for HR computation. Experiments are conducted over three standard datasets, namely PURE, UBFC, and COHFACE. The obtained mean absolute error values are 1.16 beats per minute (bpm), 1.56 bpm, and 2.10 bpm for PURE, UBFC, and COHFACE datasets, respectively, indicating the performance of the technique well above the clinically acceptable threshold. In comparison, the technique showed performance superiority over the state-of-art methods. These outcomes substantiate the potential of alternative color spaces for accurate and reliable HR monitoring from facial videos in challenging scenarios.

2

A novel fault diagnosis method for marine blower with vibration signals

Yan Guohua, Hu Yihuai, Jiang Jiawei

Polish Maritime Research

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2022

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nr 2

77--86

EN

The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.

3

An improved algorithm for efficient ocular artifact suppression from frontal EEG electrodes using VMD

Dora Chinmayee, Biswal Pradyut Kumar

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 1

148--161

EN

The Electroencephalogram (EEG) recordings from the frontal lobe of the human brain help in analyzing several important brain functions like motor functions, problem-solving skills, etc. or brain disorders. These recordings are often contaminated by high amplitude and long duration ocular artifacts (OAs) like eye blinks, flutters and lateral eye movements (LEMs), hence corrupting a considerable segment of EEG. In this study, an enhanced version of signal decomposition scheme i.e. Variational Mode Decomposition (VMD) based algorithm is used for suppression of OAs. The signal decomposition is preceded by identification of ocular artifact corrupted segment using Multiscale modified sample entropy (mMSE). The band limited intrinsic mode functions (BLIMFs) are obtained using predefined K (number of required BLIMFs) and α (balancing parameter). These parameters help to detrend the EEG segment in yielding the low frequency and high amplitude BLIMFs related to OA efficiently. Upon identifying OA components from the BLIMFs and estimating OA, it is regressed with the contaminated EEG to obtain the clean EEG. The proposed VMD based algorithm provides an improved performance in comparison with the existing single channel algorithms based on Empirical mode decomposition (EMD) and Ensembled EMD (EEMD) and multi-channel algorithms like Independent component analysis (ICA) and wavelet enhanced ICA for artifact suppression and is also able to overcome their limitations. The significance of the algorithm are: (1) no additional reference EOG channel requirement, (2) OA artifact based thresholds for identification and estimation from the mode functions obtained using VMD, and (3) also address the flutter artifacts.

4

Diagnostic factors for opened and closed kinematic chain of vibroarthrography signals

Machrowska Anna, Karpiński Robert, Krakowski Przemysław, Jonak Józef

Applied Computer Science

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2019

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Vol. 15, no 3

34--44

EN

The paper presents results of preliminary research of vibroarthrography signals recorded from one healthy volunteer. The tests were carried out for the open and closed kinematic chain in the range of motion 90° – 0° – 90°. Analysis included initial signal filtration using the EMD algorithm. The aim was to investigate the occurrence of differences in the values of selected energy and statistical parameters for the cases studied.

5

Vibration-based cavitation detection in centrifugal pumps

Hajnayeb A., Azizi R., Ghanbarzadeh A., Changizian M.

Diagnostyka

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2017

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Vol. 18, No. 3

77--83

EN

Cavitation is a common cause of failure in centrifugal pumps. Because of interaction of several mechanical parts and fluid, the vibration signal of a centrifugal pump is complicated. In this paper, the vibrations of a transparent-casing centrifugal pump are studied. Three states are studied experimentally: no cavitation, limited cavitation and developed cavitation. Each case was also confirmed by visually inspecting the cavitation bubbles. The vibrations of the pump was acquired by using an accelerometer that was attached to the casing. Discrete wavelet transform (DWT) analysis and empirical mode decomposition (EMD) are used to extract classification features from the acquired signals. Using these features, an artificial neural network (ANN) successfully diagnosed the cavitation condition of the pump. Finally, EEMD is also implemented. The results showed the success of EMD and DWT in cavitation diagnosis. The output of EEMD does not show significant change comparing to EMD.