Aviation Metrics Runner
The Aviation Metrics Runner processes biometric, simulation, and eye-tracking data collected during aviation training sessions. It produces scored outputs covering cognitive load, lookout scan behaviour, flight-path adherence, circuit performance, and more. These outputs feed downstream dashboard transforms that render instructor-facing dashboards.
Note: Several node templates in this runner (notably
cognitive-load-metrics) are domain-agnostic and may be extracted into their own runner in a future release.
Available Node Templates
1. am-preprocess (Transform Node)
Pre-processes raw biometric and simulation data into the aligned format expected by downstream analytics nodes.
Typical usage: First node in a legacy aviation pipeline; aligns timestamps and normalises column names.
2. rapid-preprocess (Transform Node)
Rapid-format pre-processing that merges biometric and simulation streams into a single JSON payload containing bio_input and sim_input arrays.
Typical usage: Entry point for RAPID MACE pipelines. Downstream nodes such as
cognitive-load-metrics,lookout-scan-metrics, andphase-segmentconsume its output.
3. phase-segment (Transform Node)
Segments a session into named flight phases (e.g. take-off, upwind, downwind, final approach) based on simulation telemetry.
Typical usage: Provides per-phase slices to
cognitive-load-metrics,lookout-scan-metrics, and other analytics nodes.
4. cognitive-load-metrics (Processing Node)
Computes a cognitive load score, timeline, and zone breakdown from biometric data using an XGBoost regression model over sliding windows of eye-tracking and heart-rate features. Accepts optional smoothing and classification configuration.
Typical usage: Placed after
rapid-preprocess(and optionallyphase-segment) to produce cognitive load outputs consumed by timeline and dashboard transforms. See the cognitive-load-metrics guide.
5. lookout-scan-metrics (Processing Node)
Analyses gaze data to score lookout scan behaviour against expected scan patterns for the current flight phase.
6. timeline-metrics (Processing Node)
Aggregates multiple upstream metric timelines (cognitive load, lookout scan, etc.) into a unified timeline structure for dashboard rendering.
7. weather-overview-metrics (Processing Node)
Extracts and summarises weather conditions from simulation telemetry for contextual display on dashboards.
8. gaze-eye-metrics (Processing Node)
Classifies 3D gaze direction data into spatial zones (Far, Near, Left, Right) using KMeans clustering with Mahalanobis distance augmentation. Produces overall gaze percentages, raw gaze vectors, and a per-second timeline. Single-user scoped.
Typical usage: Placed after
rapid-preprocess(and optionallyphase-segment) to produce gaze distribution outputs consumed by dashboard transforms. See the gaze-eye-metrics guide.
9. flight-path-metrics (Processing Node)
Evaluates flight-path adherence by comparing actual trajectory data against reference profiles.
10. circuit-scoring (Processing Node)
Scores individual flying circuit segments (take-off, upwind, downwind, final turn, final approach) against configurable criteria.
11. am-transform-circuits-dashboard (Transform Node)
Transforms scored analytics outputs into the schema expected by the circuits dashboard.