# Video-Based Gaze Detection for Oculomotor
## 4 Discussion  
In this study, we introduced a video-based gaze detection model, comprising an independent video-based NeuraLight Gaze Estimation Model (NLGEM) and an additional Calibrated Gaze Model (CGM), which uses the target locations of visual stimuli for ongoing real-time calibration during test sessions. The NLGEM model architecture is based on recently published works (Figure 2) [28], with the exception of training the model on labels measured using an IR tracker and the recruitment of a dedicated cohort for the training and test sets. For validation, we tested our gaze detection model on an independent cohort of 259 participants (see Table 1) who performed horizontal and vertical pro-saccade tasks while sitting at a distance of approximately 60 cm from a display, with no head restraint. Compared to the use of a specialized IR eye tracker as a reference ground truth, we found that the NLGEM alone achieved an absolute visual angle accuracy (MAE) of 4.80 degrees. While such an accuracy level is comparable to that of some of the leading IR eye trackers available at present [23; 24] and may be sufficient for applications that require general visual attention direction or saliency distribution on display, most eye tracking applications require higher accuracy, especially in medical applications (e.g., for the measurement of eye movements in patients with various diseases and oculomotor symptoms). Most abnormal volumetric measures (OMs) that are present in patients are based on measuring eye movements in response to visual stimulation tasks. Therefore, we added the CGM on top of the NLGEM to leverage the information provided by the visual stimuli, namely the target location. When testing the performance of NLGEM and CGM together (NLGEM + CGM), we achieved an absolute gaze detection accuracy (MAE) of 1.80 degrees in the norm, as well as an MAE of 0.93 degrees in the horizontal direction and 1.31 degrees vertically, as detailed in Table 2. When aiming to measure eye movements, the separation into horizontal and vertical directions is required as, in some movements (e.g., saccades), horizontal and vertical movements involve different pathways [11]. Therefore, the accuracy estimations in this work for the horizontal and vertical axes are useful when assessing OMs based on mechanisms that depend on the movement direction. The performance of the NLGEM + CGM approach was within the systematic errors of the Tobii Pro Fusion eye tracker (0.3 degrees) when taking into consideration the stimuli target size, which was \(1.35\pm 0.22\ deg\). Given the accuracy level of our reference measurement (Tobii, 0.3 degrees) and the size of the target displayed (\(\sim\)1.35 degrees), we suggest that our gaze detection model accuracy fulfils the requirements of various OM extraction applications, such as measuring oculomotor abnormalities in patients with different diseases,  
Figure 9: The mean absolute error (MAE) of the video-based NLGEM gaze estimation (blue) and of the video-based NLGEM + CGM gaze estimation (red) as a function of the patient’s age: (**a**) The absolute norm error; (**b**) The error in the horizontal axis; (**c**) The error in the vertical axis. The solid lines represent the corresponding linear fits. using only a retail-grade webcam and without the need to fix the subject's head. With the used video sample rate of 60 frames per second, the maximal error in time for a single sample is 16.6 ms, allowing for the confident detection of abnormalities in OMs, whose time scale is ~100 ms and whose visual angle scale is larger than our calculated errors (e.g., the Square Wave Jerks [34] rate, Saccadic Latency, Saccadic Amplitude, Saccadic Error Rate, and so on). The impact of age on our video-based gaze detection models was estimated according to an increase in the MAE with the age of the patient, presenting an average rate of 0.013 \({}^{deg}\)/\({}_{yr}\). The increase in the gaze estimation MAE with age was similar for the NLGEM and NLGEM + CGM models, as well as in all direction variances examined.