# Machine Learning-Based Detection of Cognitive Impairment from Eye-Tracking in Smooth Pursuit Tasks
## 5 Discussion
In future iterations of this work, we plan to explore methods for increasing the cognitive load of the smooth pursuit task while retaining the minimalistic design that underpins its feasibility and accessibility. One promising direction is inspired by the dual-task study  
Figure 6: Traces from the most important smooth pursuit tasks performed by a subject without cognitive impairment (Figures **(a,b)**) and a subject with possible AD (Figures **(c,d)**). The black dotted lines represent the movement of the stimulus on the screen, while the red and blue lines show the eye movement traces of the right and left eyes, respectively. **(a)** Trace for the fast horizontal smooth pursuit task (period 1600 ms, frequency \(\approx\) 0.625 Hz) performed by HC subject. **(b)** Trace for the slow vertical smooth pursuit task (period 4800 ms, frequency \(\approx\) 0.21 Hz) performed by HC subject. **(c)** Trace for the fast horizontal smooth pursuit task performed by AD subject. **(d)** Trace for the slow vertical smooth pursuit task performed by AD subject. presented by [PERSON] et al. [5], in which participants performed simple arithmetic tasks (solving visually or auditorily presented simple equations) while tracking a moving target. Their results suggest that adding such cognitive demands can increase the task's sensitivity to early-stage impairment, particularly in domains related to working memory and attention. Therefore, incorporating additional cognitive load into the task may improve its ability to detect cognitive impairment at earlier stages. Inspired by this, we are considering augmenting the current paradigm with non-verbal cognitive load conditions--such as mental arithmetic or pattern recognition tasks--delivered visually and without requiring verbal or manual responses. This would preserve the low-burden contact-free nature of our method while potentially increasing its sensitivity. Importantly, such modifications would be designed to avoid additional stimulus modalities (e.g., auditory input), which may not be feasible in all settings. Additionally, while our current work focused solely on smooth pursuit, future implementations may incorporate additional simple tasks that could be completed in a single session without excessively increasing the burden. Prior work by [PERSON] et al. [19] showed that including multiple gaze-based tests targeting various cognitive domains yielded higher classification performance. Longitudinal data collection and multimodal integration (e.g., combining smooth pursuit metrics with reading- or fixation-based features [12, 22]) are also being considered to improve the classification performance. Overall, our goal is to maintain the simplicity and efficiency of the current protocol while gradually enhancing its diagnostic value through minimally intrusive cognitive extensions. Furthermore, a recent review by [PERSON] et al. [23] outlines the broader diagnostic potential of oculomotor assessments across neurodegenerative diseases, noting that abnormalities in saccades, fixations, and pursuit movements may each carry disease-specific signatures. Importantly, they emphasise the need to bridge experimental protocols and clinically feasible tools--a gap our current method helps address by using an accessible, scalable design.