Wearable eye-trackers offer exciting advantages over screen-based systems, but their use in research settings has been hindered by significant analytic challenges as well as a lack of published performance measures among competing devices on the market. In this article, we address both of these limitations. We describe (and make freely available) an automated analysis pipeline for mapping gaze data from an egocentric coordinate system (i.e. the wearable eye-tracker) to a fixed reference coordinate system (i.e. a target stimulus in the environment). This pipeline allows researchers to study aggregate viewing behavior on a 2D planar target stimulus without restricting the mobility of participants. We also designed a task to directly compare calibration accuracy and precision across 3 popular models of wearable eye-trackers: Pupil Labs 120Hz Binocular glasses, SMI ETG 2 glasses, and the Tobii Pro Glasses 2. Our task encompassed multiple viewing conditions selected to approximate distances and gaze angles typical for short-to mid-range viewing experiments. This work will promote and facilitate the use of wearable eye-trackers for research in naturalistic viewing experiments.All analyses were completed using R v3.4.0 (R Core Team 2017) , with data formatting using the dplyr package (Wickham et al. 2017) . All linear mixed effects models were performed using the lme4 package (Bates et al. 2015) , and follow-up pairwise comparisons were performed using the lsmeans package (Lenth and Others 2016) . All results plots were created using ggplot2 (Wickham 2009) , ggpubr (Kassambara 2017) , and ggsignif (Ahlmann-Eltze 2017) packages for R . Overall PerformanceThe percentage of removed outlier (>5° from target location) gaze points during preprocessing differed by eye-tracker model. Statistical comparisons revealed the mean percentage of valid gaze points for Pupil Labs (mean: 97.1%; SE: 0.9) was significantly lower than SMI (mean: 98.7%; SE: 0.4) and Tobii (mean: 98.2%; SE: 0.4) (p < 0.001; no significant difference between Tobii and SMI).However, given that all models retained > 97% of all gaze points, this difference had a negligible effect on interpretation of subsequent analyses.We first averaged across all distances and gaze angle conditions, and tested the overall relationships between eye-tracker model and accuracy, and eye-tracker model and precision. AccuracyWe fit a linear mixed effects model to test the relationship between accuracy and eye-tracker. This model included eye-tracker as a fixed effect and subject as a random effect. Eye-tracker was a significant predictor of accuracy ( F (2,78) = 7.44, p < .001) in this model. Follow-up pairwise comparisons between eye-trackers revealed that the Pupil Labs eye-tracker was significantly more accurate than SMI ( t (78) = 2.40, p < .05) and Tobii ( t (78) = 3.81, p < .001). All other comparisons were non-significant at p > .1; see Table 2 , and Fig 4.A .