Dataset Information

EasyEyes - A new method for accurate fixation in online vision testing.

ABSTRACT: Online methods allow testing of larger, more diverse populations, with much less effort than in-lab testing. However, many psychophysical measurements, including visual crowding, require accurate eye fixation, which is classically achieved by testing only experienced observers who have learned to fixate reliably, or by using a gaze tracker to restrict testing to moments when fixation is accurate. Alas, both approaches are impractical online as online observers tend to be inexperienced, and online gaze tracking, using the built-in webcam, has a low precision (±4 deg). EasyEyes open-source software reliably measures peripheral thresholds online with accurate fixation achieved in a novel way, without gaze tracking. It tells observers to use the cursor to track a moving crosshair. At a random time during successful tracking, a brief target is presented in the periphery. The observer responds by identifying the target. To evaluate EasyEyes fixation accuracy and thresholds, we tested 12 naive observers in three ways in a counterbalanced order: first, in the laboratory, using gaze-contingent stimulus presentation; second, in the laboratory, using EasyEyes while independently monitoring gaze using EyeLink 1000; third, online at home, using EasyEyes. We find that crowding thresholds are consistent and individual differences are conserved. The small root mean square (RMS) fixation error (0.6 deg) during target presentation eliminates the need for gaze tracking. Thus, this method enables fixation-dependent measurements online, for easy testing of larger and more diverse populations.

SUBMITTER: Kurzawski JW

PROVIDER: S-EPMC10718086 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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EasyEyes - A new method for accurate fixation in online vision testing.

Kurzawski Jan W JW Pombo Maria M Burchell Augustin A Hanning Nina M NM Liao Simon S Majaj Najib J NJ Pelli Denis G DG

Frontiers in human neuroscience 20231129

Online methods allow testing of larger, more diverse populations, with much less effort than in-lab testing. However, many psychophysical measurements, including visual crowding, require accurate eye fixation, which is classically achieved by testing only experienced observers who have learned to fixate reliably, or by using a gaze tracker to restrict testing to moments when fixation is accurate. Alas, both approaches are impractical online as online observers tend to be inexperienced, and onlin ...[more]

PMID: 38094145

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Project description:BackgroundMeasurement of muscle mass is important in the diagnosis of sarcopenia. Current measurement equipment are neither cost-effective nor standardized and cannot be used in a variety of medical settings. Some simple measurement tools have been proposed that are subjective and unvalidated. We aimed to develop and validate a new estimation equation in a more objective and standardized way, based on current proven variables that accurately reflect muscle mass.MethodsCross-sectional analysis with The National Health and Nutrition Examination Survey database for equation development and validation. Overall, 9875 participants were included for development (6913 participants) and validation (2962 participants), for whom the database included demographic data, physical measurements, and main biochemical indicators. Appendicular skeletal muscle mass (ASM) was estimated by dual-energy x-ray absorptiometry (DXA) and low muscle mass was defined by reference to five international diagnostic criteria. Linear regression was used to estimate the logarithm of the actual ASM from demographic data, physical measurements, and biochemical indicators.ResultsThis study of 9875 participants comprised 4492 females (49.0%), with a weighted mean (SE) age of 41.83 (0.36) years and range of 12 to 85 years. The estimated ASM equations performed well in the validation data set. The variability in estimated ASM was low compared with the actual ASM (R2 : Equation 1 = 0.91, Equation 4 = 0.89), with low bias (median difference: Equation 1 = -0.64, Equation 4 = 0.07; root mean square error: Equation 1 = 1.70 [1.69-1.70], Equation 4 = 1.85 [1.84-1.86]), high precision (interquartile range of the differences: Equation 1 = 1.87, Equation 4 = 2.17), and high efficacy in diagnosing low muscle mass (area under the curve: Equation 1 = 0.91 to 0.95, Equation 4 = 0.90 to 0.94).ConclusionsThe estimated ASM equations are accurate and simple and can be routinely applied clinically to estimate ASM and thus assess sarcopenia.

Dataset Information

EasyEyes - A new method for accurate fixation in online vision testing.

Publications

EasyEyes - A new method for accurate fixation in online vision testing.

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