Real-time principal component pursuit

Asthma is heterogeneous but accessible biomarkers to distinguish relevant phenotypes remain lacking, particularly in non-Type 2 (T2)-high asthma. Moreover, common clinical characteristics in both T2-high and T2-low asthma (e.g., atopy, obesity, inhaled steroid use) may confound interpretation of putative biomarkers and of underlying biology. This study aimed to identify volatile organic compounds (VOCs) in exhaled breath that distinguish not only asthmatic and non-asthmatic subjects, but also atopic non-asthmatic controls and also by variables that reflect clinical differences among asthmatic adults. A total of 73 participants (30 asthma, eight atopic non-asthma, and 35 non-asthma/non-atopic subjects) were recruited for this pilot study. A total of 79 breath samples were analyzed in real-time using an automated portable gas chromatography (GC) device developed in-house. GC-mass spectrometry was also used to identify the VOCs in breath. Machine learning, linear discriminant analysis, and principal component analysis were used to identify the biomarkers. Our results show that the portable GC was able to complete breath analysis in 30 min. A set of nine biomarkers distinguished asthma and non-asthma/non-atopic subjects, while sets of two and of four biomarkers, respectively, further distinguished asthmatic from atopic controls, and between atopic and non-atopic controls. Additional unique biomarkers were identified that discriminate subjects by blood eosinophil levels, obese status, inhaled corticosteroid treatment, and also acute upper respiratory illnesses within asthmatic groups. Our work demonstrates that breath VOC profiling can be a clinically accessible tool for asthma diagnosis and phenotyping. A portable GC system is a viable option for rapid assessment in asthma.

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An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities

Sensors ◽

10.3390/s21082869 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2869

Author(s):

Jiaen Wu ◽

Kiran Kuruvithadam ◽

Alessandro Schaer ◽

Richie Stoneham ◽

George Chatzipirpiridis ◽

...

Keyword(s):

Real Time ◽

Inertial Sensor ◽

Neurological Diseases ◽

Principal Component ◽

Sampling Frequency ◽

Powerful Analytical Tool ◽

Optical Motion ◽

Analysis System ◽

Optical Motion Capture ◽

Gait Monitoring

The deterioration of gait can be used as a biomarker for ageing and neurological diseases. Continuous gait monitoring and analysis are essential for early deficit detection and personalized rehabilitation. The use of mobile and wearable inertial sensor systems for gait monitoring and analysis have been well explored with promising results in the literature. However, most of these studies focus on technologies for the assessment of gait characteristics, few of them have considered the data acquisition bandwidth of the sensing system. Inadequate sampling frequency will sacrifice signal fidelity, thus leading to an inaccurate estimation especially for spatial gait parameters. In this work, we developed an inertial sensor based in-shoe gait analysis system for real-time gait monitoring and investigated the optimal sampling frequency to capture all the information on walking patterns. An exploratory validation study was performed using an optical motion capture system on four healthy adult subjects, where each person underwent five walking sessions, giving a total of 20 sessions. Percentage mean absolute errors (MAE%) obtained in stride time, stride length, stride velocity, and cadence while walking were 1.19%, 1.68%, 2.08%, and 1.23%, respectively. In addition, an eigenanalysis based graphical descriptor from raw gait cycle signals was proposed as a new gait metric that can be quantified by principal component analysis to differentiate gait patterns, which has great potential to be used as a powerful analytical tool for gait disorder diagnostics.

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Real-Time Principal Component Analysis

ACM/IMS Transactions on Data Science ◽

10.1145/3374750 ◽

2020 ◽

Vol 1 (2) ◽

pp. 1-36

Author(s):

Ranak Roy Chowdhury ◽

Muhammad Abdullah Adnan ◽

Rajesh K. Gupta

Keyword(s):

Principal Component Analysis ◽

Real Time ◽

Principal Component ◽

Component Analysis

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Sparsity regularized Principal Component Pursuit

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2017.7952994 ◽

2017 ◽

Author(s):

Jing Liu ◽

Pamela C. Cosman ◽

Bhaskar D. Rao

Keyword(s):

Principal Component ◽

Principal Component Pursuit

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Stable Analysis of Compressive Principal Component Pursuit

Algorithms ◽

10.3390/a10010029 ◽

2017 ◽

Vol 10 (1) ◽

pp. 29

Author(s):

Qingshan You ◽

Qun Wan

Keyword(s):

Principal Component ◽

Principal Component Pursuit

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Separating damage from environmental effects affecting civil structures for near real-time damage detection

Structural Health Monitoring ◽

10.1177/1475921717722060 ◽

2017 ◽

Vol 17 (4) ◽

pp. 850-868 ◽

Cited By ~ 7

Author(s):

William Soo Lon Wah ◽

Yung-Tsang Chen ◽

Gethin Wyn Roberts ◽

Ahmed Elamin

Keyword(s):

Damage Detection ◽

Real Time ◽

Environmental Effects ◽

Environmental Conditions ◽

Principal Component ◽

Detection Methods ◽

Real Time Monitoring ◽

Civil Structures ◽

Data Set ◽

Wide Range

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

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A ROBUST AND FRIENDLY HUMAN–ROBOT INTERFACE SYSTEM BASED ON NATURAL HUMAN GESTURES

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001410008214 ◽

2010 ◽

Vol 24 (06) ◽

pp. 847-866 ◽

Cited By ~ 5

Author(s):

MING-SHAUNG CHANG ◽

JUNG-HUA CHOU

Keyword(s):

Real Time ◽

Face Detection ◽

Fuzzy Logic Controller ◽

Tracking System ◽

Back Propagation ◽

Principal Component ◽

Working Environment ◽

Expression Recognition ◽

Linear Discriminant ◽

Human Gestures

In this paper, we design a robust and friendly human–robot interface (HRI) system for our intelligent mobile robot based only on natural human gestures. It consists of a triple-face detection method and a fuzzy logic controller (FLC)-Kalman filter tracking system to check the users and predict their current position in a dynamic and cluttered working environment. In addition, through the combined classifier of the principal component analysis (PCA) and back-propagation artificial neural network (BPANN), single and successive commands defined by facial positions and hand gestures are identified for real-time command recognition after dynamic programming (DP). Therefore, the users can instruct this HRI system to make member recognition or expression recognition corresponding to their gesture commands, respectively based on the linear discriminant analysis (LDA) and BPANN. The experimental results prove that the proposed HRI system perform accurately in real-time face detection and tracking, and robustly react to the corresponding gesture commands at eight frames per second (fps).

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Direct Analysis in Real Time by Mass Spectrometric Technique for Determining the Variation in Metabolite Profiles ofCinnamomum tamalaNees and Eberm Genotypes

The Scientific World JOURNAL ◽

10.1100/2012/549265 ◽

2012 ◽

Vol 2012 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Vineeta Singh ◽

Atul Kumar Gupta ◽

S. P. Singh ◽

Anil Kumar

Keyword(s):

Real Time ◽

Principal Component ◽

Direct Analysis ◽

Ion Source ◽

Diabetic Patients ◽

Phytochemical Analysis ◽

Active Components ◽

Cinnamomum Tamala ◽

Wide Range ◽

Metabolite Profiles

Cinnamomum tamalaNees & Eberm. is an important traditional medicinal plant, mentioned in various ancient literatures such as Ayurveda. Several of its medicinal properties have recently been proved. To characterize diversity in terms of metabolite profiles ofCinnamomum tamalaNees and Eberm genotypes, a newly emerging mass spectral ionization technique direct time in real time (DART) is very helpful. The DART ion source has been used to analyze an extremely wide range of phytochemicals present in leaves ofCinnamomum tamala. Ten genotypes were assessed for the presence of different phytochemicals. Phytochemical analysis showed the presence of mainly terpenes and phenols. These constituents vary in the different genotypes ofCinnamomum tamala. Principal component analysis has also been employed to analyze the DART data of theseCinnamomumgenotypes. The result shows that the genotype ofCinnamomum tamalacould be differentiated using DART MS data. The active components present inCinnamomum tamalamay be contributing significantly to high amount of antioxidant property of leaves and, in turn, conditional effects for diabetic patients.

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