A visual explanation of how ArenaK generates high-frequency behavioral signals that inform DT4H Twin calibration, StateK computation, and SETPOINT execution loops.
Reaction timing, challenge response, recovery rhythm, and engagement continuity are captured during structured adaptive play.
Gameplay events become resilience, fatigue, recovery, consistency, and drift signals.
Behavioral evidence updates confidence, trajectory, and adaptive interpretation inside the DT4H runtime.
StateK interprets readiness, regulation, drift, and transition posture from the updated Twin.
SETPOINT uses runtime state to choose practice posture, collect outcomes, and feed recalibration.
Response speed, precision, consistency, variability.
Difficulty shifts, strategy changes, learning under load.
Post-challenge reset, fatigue, regulation response.
Adherence, participation rhythm, repeatability.
Wearables observe physiology and activity, often without structured challenge context.
Gameplay creates intentional behavioral events for repeatable longitudinal modeling.
Behavioral evidence updates confidence, drift, and trajectory interpretation.
Practice outcomes and ArenaK signals both feed recalibration.