GazeFlow: Personalized Ambient Soundscape Generation for Passive Strabismus Self-Monitoring

TL;DR

GazeFlow is a browser-based system that uses gaze tracking and ambient sound to passively monitor eye alignment in individuals with strabismus, promoting self-awareness without active engagement.

Contribution

The paper introduces GazeFlow, a novel passive self-monitoring system utilizing personalized autoencoders and ambient audio feedback for strabismus management.

Findings

Achieved F1=0.84 for drift detection on GazeBase dataset.

Participants reported increased eye awareness (average 5.8/7).

Participants preferred ambient feedback over alerts (average 6.2/7).

Abstract

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.