feat(v1): compose temporal fall event pipeline

v1/fall_policy.py

@@ -0,0 +1,45 @@
+"""Translate per-track pose facts into state-machine evidence."""
+
+from typing import Dict
+
+from v1.evidence import PoseEvidence
+from v1.fall_state import Evidence, FallState
+
+
+class FallEvidencePolicy:
+    """Require a recent rapid drop before a horizontal pose becomes a candidate."""
+
+    def __init__(self, suspect_window_seconds: float) -> None:
+        self._suspect_window_seconds = float(suspect_window_seconds)
+        self._rapid_drop_at: Dict[str, float] = {}
+
+    def evaluate(
+        self,
+        track_id: str,
+        pose_evidence: PoseEvidence,
+        now: float,
+        state: FallState,
+    ) -> Evidence:
+        timestamp = float(now)
+        if not pose_evidence.accepted:
+            self._rapid_drop_at.pop(track_id, None)
+            return Evidence(accepted=False, is_fall_candidate=False)
+        if pose_evidence.rapid_vertical_change:
+            self._rapid_drop_at[track_id] = timestamp
+
+        candidate = False
+        if state is FallState.SUSPECT:
+            candidate = pose_evidence.horizontal_pose
+        elif pose_evidence.horizontal_pose and track_id in self._rapid_drop_at:
+            candidate = timestamp - self._rapid_drop_at[track_id] <= self._suspect_window_seconds
+
+        recovery = (
+            state in (FallState.CONFIRMED, FallState.RECOVERING)
+            and not pose_evidence.horizontal_pose
+            and not pose_evidence.rapid_vertical_change
+        )
+        return Evidence(
+            accepted=pose_evidence.accepted,
+            is_fall_candidate=candidate,
+            is_recovery_candidate=recovery,
+        )