# Video-Based Gaze Detection for Oculomotor
## 1 Introduction  
With the development of computational technology and automated machinery, eye movement measurements--specifically, gaze detection and tracking--have gained increasing interest in recent decades. Some applications of gaze detection aim to improve the user interface and engagement with various external complex controlled systems; for example, gaze-based human-computer interactions [1; 2] enable the hands-free operation of software, while safety and awareness gaze-based assessments ensure the optimal functionality of drivers [3], pilots [4], and even surgeons [5; 6]. Another set of applications intends to harness the remotely detected gaze to map the saliency of selected items in the overall field of view. Advertisement and marketing-oriented studies [7; 8] have extensively implemented gaze detection, and tourism researchers have performed gaze detection along with the measurement of other emotional and physical metrics to estimate reactions to particular items or sights [9]. Another rapidly extending set of gaze detection applications is aimed at non-invasive measurement of eye movements in patients with various diseases. As a primary apparent output of the oculomotor system, gaze detection time-series data allow for further derivation and extraction of the eye movement features directly associated with a subject's cognitive and motor functionality, enabling the detection of eye movement abnormalities that may be related to the patient's condition. Eye movement abnormalities refer to deviations or irregularities observed in the typical characteristic patterns of eye motion in healthy individuals. Such deviations may beindicative of underlying neurological, ophthalmological, or vestibular disorders, making them significant diagnostic indicators for clinicians [10]. The human eye engages in various movement types, including saccades (rapid movements between separate fixation points), smooth pursuit (continuously tracking moving objects), and nystagmus (involuntary rhythmic oscillations of the eyes). Abnormalities in these movements may manifest in various OMs in terms of accuracy, speed, co-ordination, and response patterns with respect to visual stimuli. Abnormal eye movements may be the result of factors such as damage to the neural pathways governing eye movements [11], dysfunction in the muscles responsible for ocular motion [12], or disruptions in the vestibular system (which processes spatial orientation and balance) [13]. Consequently, eye movement abnormalities are observed across a spectrum of conditions, including those affecting the central nerve system (CNS) such as multiple sclerosis [14; 15], Parkinson's disease [16; 17], and amyotrophic lateral sclerosis (ALS) [18; 19]. Therefore, extracted eye movement features (i.e., OMs) have potential as new biomarkers for the diagnosis of several CNS diseases [16; 18; 20; 21], strongly correlating with the patient's condition and disease progression [22]. Clinicians routinely examine and assess eye movements as a diagnostic tool. Monitoring the accuracy, velocity, and co-ordination of eye movements provides valuable insights into the integrity of neural circuits involved in visual processing and motor control. While clinicians have conducted eye movement examinations for decades, the OMs extracted without specialized equipment or laboratory setup are limited, mainly due to the required temporal and spatial sensitivity. For instance, Saccade movement duration (the time from initialization of the movement until the first stop of the eye) may reach values in the order of 100 ms, posing challenges for the manual detection of eye movement abnormalities. Therefore, the introduction of gaze detection tools with high spatio-temporal accuracies is expected to enable the extraction of the relevant OM, allowing for a more confident observation of eye movement abnormalities.