scholarly journals Development of Estimating Equation of Machine Operational Skill by Utilizing Eye Movement Measurement and Analysis of Stress and Fatigue

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Satoshi Suzuki ◽  
Asato Yoshinari ◽  
Kunihiko Kuronuma
Keyword(s):  
Eye Movement ◽  
Evaluation Method ◽  
Eye Gaze ◽  
Principal Component ◽  
Estimating Equation ◽  
Skill Levels ◽  
Outlier Test ◽  
Machine Operators ◽  
Skill Evaluation ◽  
Human Support

For an establishment of a skill evaluation method for human support systems, development of an estimating equation of the machine operational skill is presented. Factors of the eye movement such as frequency, velocity, and moving distance of saccade were computed using the developed eye gaze measurement system, and the eye movement features were determined from these factors. The estimating equation was derived through an outlier test (to eliminate nonstandard data) and a principal component analysis (to find dominant components). Using a cooperative carrying task (cc-task) simulator, the eye movement and operational data of the machine operators were recorded, and effectiveness of the derived estimating equation was investigated. As a result, it was confirmed that the estimating equation was effective strongly against actual simple skill levels (r=0.56–0.84). In addition, effects of internal condition such as fatigue and stress on the estimating equation were analyzed. Using heart rate (HR) and coefficient of variation of R-R interval (Cvrri). Correlation analysis between these biosignal indexes and the estimating equation of operational skill found that the equation reflected effects of stress and fatigue, although the equation could estimate the skill level adequately.

scholarly journals Exploring the Gaze Behavior of Tennis Players with Different Skill Levels When Receiving Serves through Eye Movement Information

2021 ◽  
Vol 11 (19) ◽  
pp. 8794
Author(s):  
Yen-Nan Lin ◽  
Jun Wang ◽  
Yu Su ◽  
I-Lin Wang
Keyword(s):  
Eye Movement ◽  
Contact Area ◽  
Eye Gaze ◽  
Gaze Behavior ◽  
Skill Levels ◽  
Tennis Players ◽  
The Gaze ◽  
Ball Contact ◽  
Group A ◽  
Group B

Background: The purpose of this study was to explore the gaze behavior of tennis players with different skill levels when receiving serves through eye movement information. Methods: The skill level was divided into group A (experts, with more than 10 years of playing experience) and group B (novices, with less than 2 years of playing experience). We compared the differences in gaze behavior between groups A and B at the head-shoulder, trunk-hips, arm-hand, leg-foot, racket, ball, and racket-ball contact area seven positions using the Eye-gaze Response Interface Computer Aid (ERICA) device. Data were analyzed using two-way ANOVA. Results: Compared with the novices, the experts have more gaze time in the head–shoulders, rack, and ball when serving forehand (p < 0.01). The experts also have more gaze time on the head–shoulders, trunk–hips, racket, ball, and racket–ball contact area when serving backhand (p < 0.05). Conclusions: Expert athletes have a longer stare time for a specific position, which mainly determines the direction of the ball. Tennis coaches can increase the gaze time for these four positions and improve tennis players’ ability to predict the direction of the ball.

scholarly journals A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6387
Author(s):  
Li Xu ◽  
Jianzhong Hu
Keyword(s):  
Infrared Thermography ◽  
Visual Information ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Principal Component ◽  
Defect Depth ◽  
Model Learning ◽  
Flat Bottom ◽  
Bp Network ◽  
Active Infrared Thermography

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

scholarly journals Automated Filtering of Eye Movements Using Dynamic AOI in Multiple Granularity Levels

2021 ◽  
Vol 12 (1) ◽  
pp. 49-64
Author(s):  
Gavindya Jayawardena ◽  
Sampath Jayarathna
Keyword(s):  
Eye Movements ◽  
Eye Tracking ◽  
Eye Movement ◽  
Eye Gaze ◽  
Movement Analysis ◽  
Movement Data ◽  
Bounding Boxes ◽  
Multiple Granularity ◽  
Instance Segmentation ◽  
Segmentation Models

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object detectors and object instance segmentation models to find the best model to be integrated in a real-time eye movement analysis pipeline. The authors filter gaze data that falls within the polygonal boundaries of detected dynamic AOIs and apply object detector to find bounding-boxes in a public dataset. The results indicate that the dynamic AOIs generated by object detectors capture 60% of eye movements & object instance segmentation models capture 30% of eye movements.

scholarly journals Multiple Process Capability Improvement Method based on Principal Component Analysis

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Guangqi Ying ◽  
Yan Ran ◽  
Genbao Zhang ◽  
Yuxin Liu ◽  
Shengyong Zhang
Keyword(s):  
Principal Component Analysis ◽  
Principal Components ◽  
Evaluation Method ◽  
Process Capability ◽  
Principal Component ◽  
Component Analysis ◽  
Multiple Process ◽  
Improvement Method ◽  
Capability Evaluation ◽  
Capability Improvement

For the traditional multi-process capability construction method based on principal component analysis, the process variables are mainly considered, but not the process capability, which leads to the deviation of the contribution rate of principal component. In response to the question, this paper first clarifies the problem from two aspects: theoretical analysis and example proof. Secondly, aiming at the rationality of principal components degree, an evaluation method for pre-processing data before constructing MPCI using PCA is proposed. The pre-processing of data is mainly to standardize the specification interval of quality characteristics making the principal components degree more reasonable and optimizes the process capability evaluation method. Finally, the effectiveness and feasibility of the method are proved by an application example.

4. Adaptive Gaze Control in an Observational Learning Task

2016 ◽  
Author(s):  
James Kim
Keyword(s):  
Eye Movement ◽  
Observational Learning ◽  
Eye Gaze ◽  
Relevant Information ◽  
Learning Task ◽  
Gaze Control ◽  
Visually Guided ◽  
Object Condition ◽  
Learning Tasks ◽  
Different Types

The purpose of this study was to examine factors that influence how people look at objects they will have to act upon while watching others interact with them first. We investigated whether including different types of task-relevant information into an observational learning task would result in participants adapting their gaze towards an object with more task-relevant information. The participant watched an actor simultaneously lift and replace two objects with two hands then was cued to lift one of the two objects. The objects had the potential to change weight between each trial. In our cue condition, participants were cued to lift one of the objects every single time. In our object condition, the participants were cued equally to act on both objects; however, the weights of only one of the objects would have the potential to change. The hypothesis in the cue condition was that the participant would look significantly more at the object being cued. The hypothesis for the object condition was that the participant would look significantly more (i.e. adapt their gaze) at the object changing weight. The rationale behind this is that participants will learn to allocate their gaze significantly more towards that object so they can gain information about its properties (i.e. weight change). Pending results will indicate whether or not this occurred, and has implications for understanding eye movement sequences in visually guided behaviour tasks. The outcome of this study also has implications for the mechanisms of eye gaze with respect to social learning tasks. 

scholarly journals Corrosion Detection in PSC Bridge Tendons Using Kernel PCA Denoising of Measured MFL Signals

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 5984
Author(s):  
Chang Kook Oh ◽  
Changbin Joh ◽  
Jung Woo Lee ◽  
Kwang-Yeun Park
Keyword(s):  
Prestressed Concrete ◽  
Evaluation Method ◽  
Principal Component ◽  
Concrete Bridges ◽  
Kernel Principal Component Analysis ◽  
Cross Sectional ◽  
Measuring Device ◽  
Prestressed Concrete Bridges ◽  
Significant Damage ◽  
Non Destructive

The construction of prestressed concrete bridges has witnessed a steep increase for the past 50 years worldwide. The constructed bridges exposed to various environmental conditions deteriorate all along their service life. One such degradation is corrosion, which can cause significant damage if it occurs on the main structural components, such as prestressing tendons. In this study, a novel non-destructive evaluation method to incorporate a movable yoke system with denoising algorithm based on kernel principal component analysis is developed and applied to identify the loss of cross-sectional area in corroded external prestressing tendons. The proposed method using denoised output voltage signals obtained from the measuring device appears to be a reliable and precise monitoring system to detect corrosion with less than 3% sectional loss.

Laparoscopic Skill Classification Using the Two-Third Power Law and the Isogony Principle

2017 ◽  
Author(s):  
Anna French ◽  
Timothy M. Kowalewski
Keyword(s):  
Patient Outcomes ◽  
Training Session ◽  
Human Intervention ◽  
Training Module ◽  
Surgical Skill ◽  
Scoring Method ◽  
Skill Levels ◽  
Surgical Tool ◽  
Task Time ◽  
Skill Evaluation

Surgical skill evaluation is a field that attempts to improve patient outcomes by accurately assessing surgeon proficiency. An important application of the information gathered from skill evaluation is providing feedback to the surgeon on their performance. The most commonly utilized methods for judging skill all depend on some type of human intervention. Expert panels are considered the gold standard for skill evaluation, but are cost prohibitive and often take weeks or months to deliver scores. The Fundamentals of Laparoscopic Surgery (FLS) is a widely adopted surgical training regime. Its scoring method is based on task time and number of task-specific errors, which currently requires a human proctor to calculate. This scoring method requires prior information on the distribution of scores among skill levels, which creates a problem any time a new training module or technique is introduced. These scores are not normally provided while training for the FLS skills test, and [1] has shown that FLS scoring does not lend any additional information over sorting skill levels based on task time. Crowd sourced methods such as those in [2] have also been used to provide feedback and have shown concordance with patient outcomes, however it still takes a few hours to generate scores after a training session. It is desired to find an assessment method that can deliver a score immediately following a training module (or even in real time) and depends neither on human intervention nor on task-specific probability distributions. It is hypothesized that isogony-based surgical tool motion analysis discerns surgical skill level independent of task time.

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