Strabismus affects 2-4% of the population, yet individuals recovering from corrective surgery lack accessible tools for monitoring eye alignment. Dichoptic therapies require active engagement & clinical supervision, limiting their adoption for passive self-awareness. We present GazeFlow, a browser-based self-monitoring system that uses a personalized temporal autoencoder to detect eye drift patterns from webcam-based gaze tracking & provides ambient audio feedback. Unlike alert-based systems, GazeFlow operates according to calm computing principles, morphing musical parameters in proportion to drift severity while remaining in peripheral awareness. We address the challenges of inter-individual variability & domain transfer (1000Hz research to 30Hz webcam) by introducing Binocular Temporal-Frequency Disentanglement (BTFD), Contrastive Biometric Pre-training (CBP), & Gaze-MAML. We validate our approach on the GazeBase dataset (N=50) achieving F1=0.84 for drift detection, & conduct a preliminary user study (N=6) with participants having intermittent strabismus. Participants reported increased awareness of their eye behaviour (M=5.8/7) & preference for ambient feedback over alerts (M=6.2/7). We discuss the system's potential for self-awareness applications & outline directions for clinical validation.

翻译：斜视影响着2-4%的人口，然而接受矫正手术后的个体缺乏监测眼球对准的可及工具。双眼分视疗法需要主动参与和临床监督，限制了其在被动自我感知中的应用。我们提出GazeFlow——一个基于浏览器的自我监测系统，该系统使用个性化时序自编码器从基于网络摄像头的视线追踪数据中检测眼球漂移模式，并提供环境音频反馈。与基于警报的系统不同，GazeFlow遵循平静计算原则运行，根据漂移严重程度按比例调整音乐参数，同时保持在周边感知范围内。我们通过引入双目时频解耦（BTFD）、对比生物特征预训练（CBP）和Gaze-MAML，解决了个体间差异和领域迁移（从1000Hz研究设备到30Hz网络摄像头）的挑战。我们在GazeBase数据集（N=50）上验证了该方法，实现了漂移检测F1分数=0.84，并对患有间歇性斜视的参与者进行了初步用户研究（N=6）。参与者报告对其眼部行为的感知度有所提升（平均值=5.8/7），并且相较于警报更偏好环境反馈（平均值=6.2/7）。我们讨论了该系统在自我感知应用方面的潜力，并概述了临床验证的方向。