GazeFlow: Personalized Ambient Soundscape Generation for Passive Strabismus Self-Monitoring
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arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915812315496448 |
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| author | Chandra, Joydeep Navneet, Satyam Kumar Zhang, Yong |
| author_facet | Chandra, Joydeep Navneet, Satyam Kumar Zhang, Yong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19966 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GazeFlow: Personalized Ambient Soundscape Generation for Passive Strabismus Self-Monitoring Chandra, Joydeep Navneet, Satyam Kumar Zhang, Yong Human-Computer Interaction 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. |
| title | GazeFlow: Personalized Ambient Soundscape Generation for Passive Strabismus Self-Monitoring |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.19966 |