scholarly journals Facial Landmarks Detection on Faulty Datasets with Regression Trees and Principal Component Analysis Parametrization

2018 ◽  
Author(s):  
Abner S. Nascimento ◽  
Danilo A. Oliveira ◽  
Maria Raquel L. De Couto ◽  
Iális C. De Paula Júnior
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Regression Trees ◽  
Component Analysis ◽  
Parametric Models ◽  
Essential Step ◽  
Proposed Model ◽  
Combined Use ◽  
Regression Algorithms ◽  
Missing Labels

Tracking landmarks points of the human face is an essential step for the construction of interfaces capable of taking advantage of the communicative potential of facial expressions. Many strategies based on parametric models and regression algorithms with boosting can be applied to this problem. This paper proposes a solution based on the combined use of principal component analysis and regression trees. The main purpose of the presented method is to reduce the sensibility of the system to the presence of missing labels when trained with faulty datasets, by the adoption of corrective heuristics. On such cases, the proposed model achieves performance similar to the reference results, obtained by training on fault free datasets.

scholarly journals Inertial Sensor-Based Step Length Estimation Model by Means of Principal Component Analysis

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3527
Author(s):  
Melanija Vezočnik ◽  
Roman Kamnik ◽  
Matjaz B. Juric
Keyword(s):  
Principal Component Analysis ◽  
Human Body ◽  
Inertial Sensor ◽  
Principal Component ◽  
Step Length ◽  
Component Analysis ◽  
Indoor Positioning ◽  
Estimation Model ◽  
Length Estimation ◽  
Proposed Model

Inertial sensor-based step length estimation has become increasingly important with the emergence of pedestrian-dead-reckoning-based (PDR-based) indoor positioning. So far, many refined step length estimation models have been proposed to overcome the inaccuracy in estimating distance walked. Both the kinematics associated with the human body during walking and actual step lengths are rarely used in their derivation. Our paper presents a new step length estimation model that utilizes acceleration magnitude. To the best of our knowledge, we are the first to employ principal component analysis (PCA) to characterize the experimental data for the derivation of the model. These data were collected from anatomical landmarks on the human body during walking using a highly accurate optical measurement system. We evaluated the performance of the proposed model for four typical smartphone positions for long-term human walking and obtained promising results: the proposed model outperformed all acceleration-based models selected for the comparison producing an overall mean absolute stride length estimation error of 6.44 cm. The proposed model was also least affected by walking speed and smartphone position among acceleration-based models and is unaffected by smartphone orientation. Therefore, the proposed model can be used in the PDR-based indoor positioning with an important advantage that no special care regarding orientation is needed in attaching the smartphone to a particular body segment. All the sensory data acquired by smartphones that we utilized for evaluation are publicly available and include more than 10 h of walking measurements.

scholarly journals Principal Component-Based Logistical Regression Algorithms to Predict Health care Accessibility for Texas Medicaid Gap

2021 ◽  
Author(s):  
Jinting Zhang ◽  
Xiu Wu
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Good Health ◽  
Poverty Line ◽  
Component Analysis ◽  
Health Condition ◽  
Quantitative Evidence ◽  
Regression Algorithms ◽  
Logistical Regression ◽  
Scientific Judgment

Abstract Background12 states without expanded Medicaid caused 2 million people who were under the poverty line across the U.S to be in Medicaid limbo and not eligible for subsidized health plans on the Affordable Care Act insurance exchanges. In order to amplify geographic equity, this paper aims to explore the health access for Medicaid gaps in Texas. MethodsPrincipal Component-based logistical regression algorithms (PCA-LA) is provided data visualization and comparison in between unadjusted and adjusted Medicaid programs. Initially, Principal Component Analysis (PCA) eliminated well-known multiplicity problems between explanatory variables in the application of epidemiology. Optimized the traditional logistical Regression (LR), the PCA-LA method, is considered health status (HS) as a dependent variable with 0 (“poor” health) and 1 (“good” health), fourteen social-economic indexes as independent variables. ResultsAfter Principal Component Analysis (PCA), four composite components incorporated health conditions (i.e., “no regular source of care” (NRC), “Last check up more than a year ago” (LCT)), demographic impacts (i.e., four categorized adults (AS)), education (ED), and marital status (MS). Compared to the unadjusted LA, direct adjusted LA, and PCA-unadjusted LA three methods, the PCA-LA approach exhibited objective and reasonable outcomes in presenting an Odd Ratio (OR). They included that health condition is positively significant to HS due to beyond 1 OR, and negatively significant to ED, AS, and MS due to less than 1 OR. ConclusionsThis paper provided quantitative evidence for the Medicaid gap in Texas to extend Medicaid, exposed healthcare geographical inequity, offered a sight for the Centers for Disease Control and Prevention (CDC) to raise researchable direction of the Medicaid program and make a timely scientific judgment of Texas healthcare accessibility.

Vision-Based Human Face Recognition Using Extended Principal Component Analysis

2013 ◽  
Vol 5 (4) ◽  
pp. 82-94 ◽  
Author(s):  
A. F. M. Saifuddin Saif ◽  
Anton Satria Prabuwono ◽  
Zainal Rasyid Mahayuddin ◽  
Teddy Mantoro
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Personal Identification ◽  
Human Face ◽  
Analysis Model ◽  
Human Face Recognition ◽  
Proposed Model ◽  
The Face

Face recognition has been used in various applications where personal identification is required. Other methods of person's identification and verification such as iris scan and finger print scan require high quality and costly equipment. The objective of this research is to present an extended principal component analysis model to recognize a person by comparing the characteristics of the face to those of new individuals for different dimension of face image. The main focus of this research is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background is constant. This research requires a normal camera giving a 2-D frontal image of the person that will be used for the process of the human face recognition. An Extended Principal Component Analysis (EPCA) technique has been used in the proposed model of face recognition. Based on the experimental results it is expected that proposed the EPCA performs well for different face images when a huge number of training images increases computation complexity in the database.

scholarly journals Creating a Scoring System with an Armband Wearable Device for Table Tennis Forehand Loop Training: Combined Use of the Principal Component Analysis and Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3870
Author(s):  
Wen-Lan Wu ◽  
Jing-Min Liang ◽  
Chien-Fei Chen ◽  
Kuei-Lan Tsai ◽  
Nian-Shing Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Training System ◽  
Table Tennis ◽  
Tennis Players ◽  
Combined Use ◽  
Explosive Force ◽  
Expert Novice

Background: This study presents an intelligent table tennis e-training system based on a neural network (NN) model that recognizes data from sensors built into an armband device, with the component values (performances scores) estimated through principal component analysis (PCA). Methods: Six expert male table tennis players on the National Youth Team (mean age 17.8 ± 1.2 years) and seven novice male players (mean age 20.5 ± 1.5 years) with less than 1 year of experience were recruited into the study. Three-axis peak forearm angular velocity, acceleration, and eight-channel integrated electromyographic data were used to classify both player level and stroke phase. Data were preprocessed through PCA extraction from forehand loop signals. The model was trained using 160 datasets from five experts and five novices and validated using 48 new datasets from one expert and two novices. Results: The overall model’s recognition accuracy was 89.84%, and its prediction accuracies for testing and new data were 93.75% and 85.42%, respectively. Principal components corresponding to the skills “explosive force of the forearm” and “wrist muscle control” were extracted, and their factor scores were standardized (0–100) to score the skills of the players. Assessment results indicated that expert scores generally fell between 60 and 100, whereas novice scores were less than 70. Conclusion: The developed system can provide useful information to quantify expert-novice differences in fore-hand loop skills.

scholarly journals Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Haifan Liu ◽  
Jun Wang
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Independent Component Analysis ◽  
Stock Market ◽  
Principal Component ◽  
Component Analysis ◽  
Independent Component ◽  
Chinese Stock Market ◽  
Proposed Model ◽  
Bp Model

We investigate the statistical behaviors of Chinese stock market fluctuations by independent component analysis. The independent component analysis (ICA) method is integrated into the neural network model. The proposed approach uses ICA method to analyze the input data of neural network and can obtain the latent independent components (ICs). After analyzing and removing the IC that represents noise, the rest of ICs are used as the input of neural network. In order to forect the fluctuations of Chinese stock market, the data of Shanghai Composite Index is selected and analyzed, and we compare the forecasting performance of the proposed model with those of common BP model integrating principal component analysis (PCA) and single BP model. Experimental results show that the proposed model outperforms the other two models no matter in relatively small or relatively large sample, and the performance of BP model integrating PCA is closer to that of the proposed model in relatively large sample. Further, the prediction results on the points where the prices fluctuate violently by the above three models relatively deviate from the corresponding real market data.

A Model of Aircraft Support Concept Evaluation Based on DEA and PCA

2018 ◽  
Vol 6 (6) ◽  
pp. 563-576 ◽  
Author(s):  
Bin Lin ◽  
Dong Song ◽  
Zhiyue Liu
Keyword(s):  
Principal Component Analysis ◽  
Data Envelopment Analysis ◽  
Manufacturing Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Data Envelopment ◽  
Proposed Model ◽  
Concept Evaluation ◽  
Equipment Manufacturing ◽  
Complex Equipment

Abstract With the vigorous development of equipment manufacturing industry in China, higher requirements to the equipment supportability are put forward. How to evaluate the supportability of equipments (especially the aviation equipment-aircraft) objectively and correctly is the problem to be solved in the development of aviation equipments construction, demonstration and battle application. Aimed at the needs of the supportability analysis of complex equipment systems-aircraft, a model of aircraft support concept evaluation based on DEA (data envelopment analysis) and PCA (principal component analysis) is proposed. The model is used to evaluate a certain aircraft support concept. The process and the results of evaluation show that proposed model is feasible and effective. The model is suitable for advanced aircraft support concept evaluation. The feasibility and effectiveness of the proposed model is verified by the analysis of the evaluation results. This method is applicable to the evaluation of aircraft support concepts.

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