Looking at Mind Wandering During Driving Through the Windows of PCA and t-SNE

2018 ◽  
Vol 62 (1) ◽  
pp. 1863-1867 ◽  
Author(s):  
Areen Alsaid ◽  
John D. Lee ◽  
Daniel M. Roberts ◽  
Daniela Barrigan ◽  
Carryl L. Baldwin
Keyword(s):  
Performance Measures ◽  
Principal Component ◽  
Mind Wandering ◽  
Lateral Position ◽  
Driving Behavior ◽  
Driving Safety ◽  
Steering Wheel ◽  
Attentional State ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

Mind wandering is a poorly understood phenomenon that can undermine driving safety. Driving performance measures have been found to be associated with mind wandering (e.g., steering wheel movements, standard deviation of lateral position, and speed variation). However, no one measure can fully describe the driver behavior associated with mind wandering. Therefore, in this paper we explore the effect of mind wandering on nine steering measures with data collected from a study that included nine drivers over two sessions of driving over five days. Participants were periodically probed to report their attentional state–whether they were mind wandering or focusing on the task. We used two dimensionality-reduction techniques—Principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—to visualize the dimensions underlying the nine measures. Comparing PCA to t-SNE highlights the benefits of t-SNE in revealing the fine structure that differentiates driving behavior. These visualizations show that a) driver engagement increased during roadway curve segments, and b) mind wandering manifests itself through several types of steering behavior.

scholarly journals Uniform Manifold Approximation and Projection for Clustering Taxa through Vocalizations in a Neotropical Passerine (Rough-Legged Tyrannulet, Phyllomyias burmeisteri)

Animals ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1406
Author(s):  
Ronald M. Parra-Hernández ◽  
Jorge I. Posada-Quintero ◽  
Orlando Acevedo-Charry ◽  
Hugo F. Posada-Quintero
Keyword(s):  
Principal Component Analysis ◽  
Bird Species ◽  
Principal Component ◽  
Taxonomic Status ◽  
Behavioral Traits ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Taxonomic Groups ◽  
Source Of Information

Vocalizations from birds are a fruitful source of information for the classification of species. However, currently used analyses are ineffective to determine the taxonomic status of some groups. To provide a clearer grouping of taxa for such bird species from the analysis of vocalizations, more sensitive techniques are required. In this study, we have evaluated the sensitivity of the Uniform Manifold Approximation and Projection (UMAP) technique for grouping the vocalizations of individuals of the Rough-legged Tyrannulet Phyllomyias burmeisteri complex. Although the existence of two taxonomic groups has been suggested by some studies, the species has presented taxonomic difficulties in classification in previous studies. UMAP exhibited a clearer separation of groups than previously used dimensionality-reduction techniques (i.e., principal component analysis), as it was able to effectively identify the two taxa groups. The results achieved with UMAP in this study suggest that the technique can be useful in the analysis of species with complex in taxonomy through vocalizations data as a complementary tool including behavioral traits such as acoustic communication.

Influences of Gesture-Based Mobile Phone Use While Driving

2021 ◽  
pp. 036119812110263
Author(s):  
Jianwei Niu ◽  
Yulin Zhou ◽  
Dan Wang ◽  
Xingguo Liu
Keyword(s):  
Mobile Phone ◽  
Lateral Position ◽  
Driving Safety ◽  
Control Technique ◽  
Steering Wheel ◽  
Secondary Tasks ◽  
Mobile Phone Use ◽  
Driving Speed ◽  
Gesture Control ◽  
The Impact

The use of mobile phones while driving has been a hot topic in the field of driving safety for decades. Although there are few studies on the influence of gesture control on in-vehicle secondary tasks, this study aims to investigate the impact of gesture-based mobile phone use without touching while driving from the perspective of multiple-resource workload owing to visual, auditory, cognitive, and psychomotor resource occupation. A novel gesture control technique was adopted for secondary task interactions, to recognize the gestures of drivers. An experiment was conducted to study the influences of two interaction modes, traditional touch-based mobile phone interaction and gesture-based mobile phone interaction, on driving behavior in three different cognitive level task groups. The results indicate that gesture-based mobile phone interaction can improve driving performance with regard to lateral position-keeping ability and steering wheel control; nevertheless, it has no significant impact on longitudinal metrics such as driving speed, driving speed variation, and throttle control variation. Gesture-based mobile phone interactions have a larger effect on secondary tasks with medium cognitive load but not on actual operation tasks. It was also verified that the performance of gesture-based mobile phone interaction was better in secondary mobile phone tasks such as switching (e.g., switching songs) and adjusting (e.g., adjusting volume) than the traditional interaction mode. This study provides the theoretical and experimental support for human–computer interaction using gesture-based mobile phone interactive control in future automobiles.

scholarly journals A Spectral Acceleration Approach for the Spherical Harmonics Discrete Ordinate Method

2020 ◽  
Vol 12 (22) ◽  
pp. 3703
Author(s):  
Adrian Doicu ◽  
Dmitry S. Efremenko ◽  
Thomas Trautmann
Keyword(s):  
Dimensionality Reduction ◽  
Spherical Harmonics ◽  
Principal Component ◽  
Optical Parameters ◽  
Discrete Ordinate Method ◽  
Spectral Acceleration ◽  
Discrete Ordinate ◽  
Distribution Method ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

A spectral acceleration approach for the spherical harmonics discrete ordinate method (SHDOM) is designed. This approach combines the correlated k-distribution method and some dimensionality reduction techniques applied on the optical parameters of an atmospheric system. The dimensionality reduction techniques used in this study are the linear embedding methods: principal component analysis, locality pursuit embedding, locality preserving projection, and locally embedded analysis. Through a numerical analysis, it is shown that relative to the correlated k-distribution method, PCA in conjunction with a second-order of scattering approximation yields an acceleration factor of 12. This implies that SHDOM equipped with this acceleration approach is efficient enough to perform spectral integration of radiance fields in inhomogeneous multi-dimensional media.

scholarly journals A SURVEY ON THE CURES FOR THE CURSE OF DIMENSIONALITY IN BIG DATA

2017 ◽  
Vol 10 (13) ◽  
pp. 355 ◽  
Author(s):  
Reshma Remesh ◽  
Pattabiraman. V
Keyword(s):  
Dimensionality Reduction ◽  
Input Data ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
High Dimensional ◽  
Data Sets ◽  
Learning Approaches ◽  
Data Set ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

Dimensionality reduction techniques are used to reduce the complexity for analysis of high dimensional data sets. The raw input data set may have large dimensions and it might consume time and lead to wrong predictions if unnecessary data attributes are been considered for analysis. So using dimensionality reduction techniques one can reduce the dimensions of input data towards accurate prediction with less cost. In this paper the different machine learning approaches used for dimensionality reductions such as PCA, SVD, LDA, Kernel Principal Component Analysis and Artificial Neural Network  have been studied.

scholarly journals Comparative Study of Dimensionality Reduction Techniques for Spectral–Temporal Data

Information ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Shingchern D. You ◽  
Ming-Jen Hung
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Identification Accuracy ◽  
Temporal Data ◽  
Reduction Techniques ◽  
Temporal Features ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Low Dimensional Features ◽  
Motion Picture Expert Group

This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral–temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.

scholarly journals Principal Component Analysis

2021 ◽  
Vol 54 (4) ◽  
pp. 1-34
Author(s):  
Felipe L. Gewers ◽  
Gustavo R. Ferreira ◽  
Henrique F. De Arruda ◽  
Filipi N. Silva ◽  
Cesar H. Comin ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Real World ◽  
Principal Component ◽  
Component Analysis ◽  
Basic Principles ◽  
Data Standardization ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Real World Datasets

Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA. The basic principles underlying PCA, data standardization, possible visualizations of the PCA results, and outlier detection are subsequently addressed. Next, the potential of using PCA for dimensionality reduction is illustrated on several real-world datasets. Finally, we summarize PCA-related approaches and other dimensionality reduction techniques. All in all, the objective of this work is to assist researchers from the most diverse areas in using and interpreting PCA.

scholarly journals Improving the classification accuracy using hybrid techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mamdouh Abdel Alim Saad Mowafy ◽  
Walaa Mohamed Elaraby Mohamed Shallan
Keyword(s):  
Heart Disease ◽  
Classification Accuracy ◽  
Multiple Correspondence Analysis ◽  
Heart Diseases ◽  
Principal Component ◽  
Content Type ◽  
Hybrid Techniques ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Hybrid Classification

Purpose Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a high-dimensional problem that leads to a decrease in the classification accuracy of heart data. So the purpose of this study is to improve the classification accuracy of heart disease data for helping doctors efficiently diagnose heart disease by using a hybrid classification technique. Design/methodology/approach This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis (PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results in this paper, a comparative study of the performance performed including six structures such as MLP, RBFN, MLP via FCM–MCA, MLP via FCM–PCA, RBFN via FCM–MCA and RBFN via FCM–PCA to reach to the best classifier. Findings The results show that the MLP via FCM–MCA classifier structure has the highest ratio of classification accuracy and has the best performance superior to other methods; and that Smoking was the most factor causing heart disease. Originality/value This paper shows the importance of integrating statistical methods in increasing the classification accuracy of heart disease data.

scholarly journals A Review of Dimensionality Reduction Techniques for Processing Hyper-Spectral Optical Signal

2019 ◽  
pp. 85-98 ◽  
Author(s):  
Ana del Águila ◽  
Dmitry S. Efremenko ◽  
Thomas Trautmann
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Optical Signal ◽  
Spectral Radiance ◽  
Electromagnetic Spectrum ◽  
Spectral Processing ◽  
Reduction Techniques ◽  
Hyper Spectral ◽  
Dimensionality Reduction Techniques

Hyper-spectral sensors take measurements in the narrow contiguous bands across the electromagnetic spectrum. Usually, the goal is to detect a certain object or a component of the medium with unique spectral signatures. In particular, the hyper-spectral measurements are used in atmospheric remote sensing to detect trace gases. To improve the efficiency of hyper-spectral processing algorithms, data reduction methods are applied. This paper outlines the dimensionality reduction techniques in the context of hyper-spectral remote sensing of the atmosphere. The dimensionality reduction excludes redundant information from the data and currently is the integral part of high-performance radiation transfer models. In this survey, it is shown how the principal component analysis can be applied for spectral radiance modelling and retrieval of atmospheric constituents, thereby speeding up the data processing by orders of magnitude. The discussed techniques are generic and can be readily applied for solving atmospheric as well as material science problems.

scholarly journals Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering

2019 ◽  
Vol 8 (S3) ◽  
pp. 66-71
Author(s):  
T. Sudha ◽  
P. Nagendra Kumar
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.

scholarly journals Sound Effects on Physiological State and Behavior of Drivers in a Highway Tunnel

2021 ◽  
Vol 12 ◽  
Author(s):  
Yanqun Yang ◽  
Yang Feng ◽  
Said M. Easa ◽  
Xiujing Yang ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Physiological State ◽  
Driving Behavior ◽  
Driving Safety ◽  
Steering Wheel ◽  
Vehicle Speed ◽  
Accelerator Pedal ◽  
Acoustic Environment ◽  
Highway Tunnel ◽  
Significant Difference ◽  
Sound Environment

Driving behavior in a highway tunnel could be affected by external environmental factors like light, traffic flow, and acoustic environments, significantly when these factors suddenly change at the moment before and after entering a tunnel. It will cause tremendous physiological pressure on drivers because of the reduction of information and the narrow environment. The risks in driving behavior will increase, making drivers more vulnerable than driving on the regular highways. This research focuses on the usually neglected acoustic environment and its effect on drivers' physiological state and driving behavior. Based on the SIMLAB driving simulation platform of a highway tunnel, 45 drivers participated in the experiment. Five different sound scenarios were tested: original highway tunnel sound and a mix of it with four other sounds (slow music, fast music, voice prompt, and siren, respectively). The subjects' physiological state and driving behavior data were collected through heart rate variability (HRV) and electroencephalography (EEG). Also, vehicle operational data, including vehicle speed, steering wheel angle, brake pedal depth, and accelerator pedal depth, were collected. The results indicated that different sound scenarios in the highway tunnel showed significant differences in vehicle speed (p = 0.000, η2 = 0.167) and steering wheel angle (p = 0.007, η2 = 0.126). At the same time, they had no significant difference in HRV and EEG indicators. According to the results, slow music was the best kind of sound related to driving comfort, while the siren sound produced the strongest driver reaction in terms of mental alertness and stress level. The voice-prompt sound most likely caused driver fatigue and overload, but it was the most effective sound affecting safety. The subjective opinion of the drivers indicated that the best sound scenario for the overall experience was slow music (63%), followed by fast music (21%), original highway tunnel sound environment (13%), and voice-prompt sound (3%). The findings of this study will be valuable in improving acoustic environment quality and driving safety in highway tunnels.

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