Dynamic Cumulative Visual Allostatic Load Monitor

OculoSync is a proactive, hardware-agnostic clinical-grade digital ergonomics system. It leverages advanced computer vision and time-series machine learning to calculate real-time cumulative ocular stress, preventing acute ciliary muscle spasms and dry eye syndrome before physiological failure occurs.

Continuous visual stress is invisible until damage appears. Professional developers and designers endure prolonged screen exposure, leading directly to digital eye strain. Current solutions rely on static, time-based interventions (e.g., the 20-20-20 rule) or primitive blink counting. They fail because they ignore highly granular, sub-pixel ocular distress indicators and only attempt to intervene after symptoms manifest.

OculoSync establishes a personalized biometric baseline and tracks sub-pixel ocular movements, blink quality, and environmental light sync via a standard 1080p webcam. It dynamically adapts the visual environment—triggering localized, OS-level screen interventions—the moment the user's biological metrics cross a critical stress threshold.

OculoSync operates on a 6-module pipeline engineered for biomedical precision:

Standardizes tracking to the user’s unique ocular anatomy. Uses a Perspective-n-Point (PnP) algorithm to map corneal distance and records resting palpebral fissure height to generate a personalized baseline multiplier.

Moves beyond simple blink counting. Extracts Palpebral Fissure Height to track incomplete blinks in real-time, calculating a continuous Tear Film Evaporation Risk score.

A 1D-CNN time-series model that ingests continuous ocular metrics. It calculates the Cumulative Visual Allostatic Load (CVAL) over time $t$ using the following continuous integration:

$$L_{visual}(t) = \int_{0}^{t} \left( \alpha \cdot \Delta P(t) + \beta \cdot f_{blink}(t)^{-1} + \gamma \cdot \sigma_{saccade}(t) \right) dt$$

(Where $$Delta P(t)$$ is pupillary fluctuation, $$f_{blink}(t)$$ is full-blink frequency, and $$\sigma_{saccade}$$ is saccadic jitter. Constants are tuned via Module 0).

Detects involuntary eye jitters indicating extraocular muscle fatigue. Isolates the iris contour and applies Lucas-Kanade optical flow to track sub-pixel movements.

Prevents localized phototoxicity. Maps the average RGB/Luma values of the active screen window against webcam-detected pupil diameter. High variance logs a "Phototoxic Stress Event."

Calculates a real-time Confidence Interval (CI) based on ambient pixel lux. If CI drops below 80% (e.g., low light), OS-level screen adjustments halt to prevent erratic flashing, ensuring responsible and safe AI behavior.

• Computer Vision: MediaPipe (468-point Face Mesh), OpenCV (Lucas-Kanade Optical Flow)
• Machine Learning & Math: PyTorch (1D-CNN), NumPy, SciPy (Continuous Integration)
• OS Integration: mss (Active Window Luma Capture), Native OS display APIs
• Frontend/Telemetry: FastAPI, React, Recharts (Biometric Dashboard)