## 3 Results
### Eye Metric Classification
#### 3.3.2 Eye Movement Patterns During Reading  
[PERSON] et al. (2017) used eye movement data to classify reading behaviours into five patterns: speed reading, slow reading, in-depth reading, skim-and-skip, and keyword spotting [19]. Using features including fixation duration, saccade length, and landing positions, an SVM classifier achieved high accuracy, particularly for keyword spotting (96.20%) and in-depth reading (93.75%). However, speed and slow reading were less precise due to overlapping behaviours. The inclusion of an \"unknown\" class improved classification reliability, demonstrating the potential of low-cost eye trackers for identifying reading styles. Despite its success, the study's reliance on predefined behaviours in controlled settings limits its generalisability to real-world reading scenarios.  
The studies by [PERSON] et al. [20] and [PERSON] et al. [21] highlight the potential of machine learning in eye movement analysis, with distinct but complementary approaches. [PERSON] et al. employ an LSTM-based model to classify reading behaviours into memory and reasoning-based tasks, achieving high accuracies (95.39% and 94.5%) and demonstrating its value for cognitive research and AI-assisted healthcare. [PERSON] et al., on the other hand, introduce a lightweight CNN framework for real-time gaze estimation and eye-event detection, optimised for resource-constrained devices, offering scalability and efficiency without sacrificing accuracy.  
[PERSON] et al. used linked linear mixed models (LMMs) to analyse how word properties, such as length, frequency, and predictability, impact fixation durations during reading [22]. Shorter, frequent, and predictable words reduced fixation durations, while durations peaked at word centres and were shorter at the boundaries of the word (IOVP effect). Skipping patterns showed that skipped word properties affected subsequent fixation locations. Decomposing fixation location into predicted (psycholinguistic) and residual components revealed that random oculomotor errors largely drive the IOVP effect, refining our understanding of cognitive and visual processes in reading.  
[PERSON] et al. introduce a low-cost, webcam-based eye-tracking system using deep learning and computer vision techniques to track eye movements, specifically pupil centre movement and eye corner positioning [23]. The system employs a coarse-to-fine approach with a Haar Cascade Classifier for face detection and a U-Net-based network for pupil localisation. A comparison with a commercial eye tracker (SMI RED250) revealed no significant difference in eye-movement-event switching accuracy (\(p<0.05\)), with a high correlation (Pearson coefficient = \(0.707\pm 0.112\)). The system was applied in a Chinese reading test, demonstrating significant differences in reading performance between young, healthy older adults, and those with mild cognitive impairment, indicating its potential as an accessible and affordable tool for cognitive function assessment and eye-tracking research, particularly in low-cost settings.  
[PERSON] et al. demonstrated that probabilistic language models, such as n-grams and RNNs, outperform cloze completion probability (CCP) in predicting fixation durations during reading [24]. Language models accounted for a greater variance in metrics like gaze duration (GD) and single-fixation duration (SFD), with n-grams excelling in syntactic predictions and RNNs capturing next-word preprocessing. In the Potsdam Sentence Corpus, for example, n-grams explained up to 8% of GD variance compared to CCP's 3-4%. These findings highlight language models' superiority in capturing cognitive processes during reading, especially when combining predictions across models.