## 4 Discussion  
AHP is a compensatory mechanism that helps individuals optimize visual input in response to visual impairments. This phenomenon is evident in conditions such as nystagmus and strabismus, where patients adopt specific head positions to reduce visual disturbances. For example, patients with nystagmus often present abnormal head positions, typically as a head turn, to minimize involuntary eye movements and improve focus [49; 50]. Understanding and accurately measuring AHP are important for evaluating post-treatment changes and planning potential surgical interventions, especially in pediatric populations. Timely interventions not only improve visual function but also prevent long-term musculoskeletal issues resulting from untreated ocular torticollis. Over time, various tools have been developed to measure AHP, ranging from traditional manual goniometers to advanced electronic, magnetic, ultrasound-based systems and visual-based solutions like Microsoft Kinect and smartphone applications. Despite their availability, these tools are hindered by limitations, such as issues with precision, high costs, and practical challenges in clinical settings which limit their accessibility to a broader population. In response to the need for a practical, efficient, and cost-effective tool capable of delivering clinically valid assessments, the primary objective of this study was to assess the accuracy of more accessible ophthalmological tools--digital goniometer and the iOS-based eyeTilt application--against a gold-standard MoCap system. The results demonstrated that both tools assessed in this study provided accurate measurements comparable to those obtained from MoCap. High PCC (>0.7) and \(p\)-values exceeding the significance threshold indicated very strong inter-system agreement, which was further supported by the corresponding RMSE values. Notably, the RMSE was lower for head rotation (3.43\({}^{\circ}\)) compared to head inclination (5\({}^{\circ}\)). Bland-Altman plots also confirmed the general agreement between the measurement tools, with 95% of data points within the confidence interval. The findings align with the existing literature that supports the clinical utility of digital tools for assessing AHP. Although manual goniometers have long been used in clinical practice to effectively measure head posture and in diagnosing conditions like torticollis or strabismus [51; 52], advancements in technology have introduced new tools, such as digital devices and smartphone applications, as significant steps toward more precise, accessible, and cost-effective solutions for assessing and monitoring AHP. Digital goniometers address many of the limitations of traditional tools by offering enhanced precision with \begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **ROM (AHP Group)** & **ROM (CG)** \\ \hline Right Rotation (\({}^{\circ}\)) & 67.7 \(\pm\) 11.4 & 61.3 \(\pm\) 10.1 \\ Left Rotation (\({}^{\circ}\)) & 61.7 \(\pm\) 12.3 & 67.48 \(\pm\) 9.0 \\ Flexion (\({}^{\circ}\)) & 50.49 \(\pm\) 10.7 \({}^{\ast}\) & 42.2 \(\pm\) 12.8 \\ Extension (\({}^{\circ}\)) & 43.0 \(\pm\) 18.1 & 33.6 \(\pm\) 9.4 \\ Right Lateral Bending (\({}^{\circ}\)) & 38.5 \(\pm\) 10.9 & 38.7 \(\pm\) 9.1 \\ Left Lateral Bending (\({}^{\circ}\)) & 36.4 \(\pm\) 9.6 & 35.5 \(\pm\) 8.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean and standard deviation of ROM values for each task. \({}^{\ast\ast\ast}=p<0.05\) for the unpaired \(t\)-test. digital readouts, eliminating parallax errors, and minimizing user dependency for accurate positioning [53]. Moreover, they can provide real-time feedback, dynamic angle tracking, and data storage, which improve both accuracy and clinical efficiency. Smartphone applications, on the other hand, use built-in sensors such as accelerometers and gyroscopes to measure head rotation and inclination. These apps eliminate the need for precise manual positioning and reduce user error while offering real-time data collection and analysis [54; 55].