Glances and stares: Validating the feelings of patients with thyroid-associated orbitopathy

2017 ◽  
Vol 35 (3) ◽  
pp. 211-216
Author(s):  
James D Morgante ◽  
Catherine J Hwang ◽  
Helene Chokron-Garneau ◽  
Kenneth Steinsapir ◽  
Robert Goldberg ◽  
...  

This investigation was aimed at validating the feelings of patients with thyroid-associated orbitopathy (TAO), who commonly report psychosocial impairments due to the cosmetic disfigurement caused by the disease. In all, 50 adults, equally divided between two experimental conditions, participated. Adults’ visual behavior was recorded with a corneal reflection eye-tracking system as they viewed side-by-side photograph pairings of affected and non-affected individuals’ upper facial region. Adults in Experiment 1 viewed photographs of patients before corrective surgery and those in Experiment 2 viewed photographs of patients after corrective surgery. Visual behavior measures of interest included the number of stares and cumulative time spent staring. Adults appear to differentially attend to patients with TAO, staring more often and longer at these individuals, regardless of surgical correction. TAO patients’ feeling of self-consciousness and being more concerned about their appearance may be due to differential persistence of fixations (i.e. staring) from their peers.

Author(s):  
Paul A. Wetzel ◽  
Gretchen M. Anderson ◽  
Barbara A. Barelka

We conducted an 18 month study on the implementation and evaluation of a portable eye tracking system in a training environment. The system was used by F-16 simulator instructors to provide instructional feedback to F-16 student pilots during flight simulator training missions. The head-mounted components of the eye tracking system consist of a two-dimensional eye position measurement system and a miniature video scene camera. When displayed on a video monitor, a cursor corresponding to the student's line of sight allows the instructor to follow the student's visual scan pattern. The portable system can be used in a number of environments and applications to objectively assess visual behavior, to develop effective visual behavior, and to enhance training. In addition, the companion, real-time, automated analysis system can analyze, quantify, and summarize scan path behavior from recorded videotape.


Author(s):  
Henrique De Oliveira Castro ◽  
Gibson Moreira Praça ◽  
Gustavo De Conti Teixeira Costa ◽  
Gustavo Ferreira Pedrosa ◽  
Pablo Juan Greco

DOI: http://dx.doi.org/10.5007/1980-0037.2016v18n6p638 This study aims to verify the visual behavior using the analysis of both quantity and duration of visual fixation and the quality of the DM in volleyball players and athletes from other sports in extreming attacking (EA), cental attack (CA), setting (SE) and blocking (BL) situations. The visual behavior was analyzed during the evaluation of volleyball scenes with an eye-tracking system. The first decision the volunteer voiced was determined as his solution for the scene presented and it was used for analyzing the DM quality. For the quantity of visual fixations, the independent t-test was used to compare groups in the following situtations, EA, SE and BL. For analyzing the quality of DM, the chi-square test of proportions was used. Moreover, for the analysis of the duration and number of visual fixations, the independent t test was addopet (or Mann-Whitney test when some of the assumptions were not met for parametric analysis). Significantly higher values were observed on duration of visual fixations in athletes from other sports comparing to volleyball players at CA (p = 0.042). The DM were significantly different in EA (p = 0.024) and CA (p = 0.001) between groups, showing higher frequency of correct answers for the volleyball players. We conclude that volleyball players perform faster fixations in situations of CA and take more correct decisions in attacking situation (EA and CA) compared to athletes from other sports.


Author(s):  
Paul A. Wetzel ◽  
Gretchen Krueger-Anderson ◽  
Christine Poprik ◽  
Peter Bascom

2010 ◽  
Vol 36 (8) ◽  
pp. 1051-1061 ◽  
Author(s):  
Chuang ZHANG ◽  
Jian-Nan CHI ◽  
Zhao-Hui ZHANG ◽  
Zhi-Liang WANG

Author(s):  
Federico Cassioli ◽  
Laura Angioletti ◽  
Michela Balconi

AbstractHuman–computer interaction (HCI) is particularly interesting because full-immersive technology may be approached differently by users, depending on the complexity of the interaction, users’ personality traits, and their motivational systems inclination. Therefore, this study investigated the relationship between psychological factors and attention towards specific tech-interactions in a smart home system (SHS). The relation between personal psychological traits and eye-tracking metrics is investigated through self-report measures [locus of control (LoC), user experience (UX), behavioral inhibition system (BIS) and behavioral activation system (BAS)] and a wearable and wireless near-infrared illumination based eye-tracking system applied to an Italian sample (n = 19). Participants were asked to activate and interact with five different tech-interaction areas with different levels of complexity (entrance, kitchen, living room, bathroom, and bedroom) in a smart home system (SHS), while their eye-gaze behavior was recorded. Data showed significant differences between a simpler interaction (entrance) and a more complex one (living room), in terms of number of fixation. Moreover, slower time to first fixation in a multifaceted interaction (bathroom), compared to simpler ones (kitchen and living room) was found. Additionally, in two interaction conditions (living room and bathroom), negative correlations were found between external LoC and fixation count, and between BAS reward responsiveness scores and fixation duration. Findings led to the identification of a two-way process, where both the complexity of the tech-interaction and subjects’ personality traits are important impacting factors on the user’s visual exploration behavior. This research contributes to understand the user responsiveness adding first insights that may help to create more human-centered technology.


Author(s):  
Bin Li ◽  
Yun Zhang ◽  
Xiujuan Zheng ◽  
Xiaoping Huang ◽  
Sheng Zhang ◽  
...  

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