Fix sleep score parsing, dashboard body battery, segment direction

- Garmin sync: read sleepScores from dailySleepDTO (Garmin nests it there),
  so sleep score is actually stored instead of always null
- Dashboard: pass YYYY-MM-DD to the intraday endpoint (was a full ISO
  timestamp), so the body-battery tile populates
- Segment matching: follow the segment in its created direction with a
  path-length sanity check, so out-and-back routes no longer match an early
  start pass to a late finish (the >1h bogus segment times)

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

backend/app/services/garmin_connect_sync.py

@@ -555,8 +555,10 @@ def _parse_day(stats, sleep_data, hrv_data) -> dict:
         if spo2 and 50 < float(spo2) <= 100:
             row["spo2_avg"] = float(spo2)
 
-        # Sleep score — structure varies across firmware
-        scores = sleep_data.get("sleepScores") or sleep_data.get("sleepScore")
+        # Sleep score — Garmin nests it under dailySleepDTO.sleepScores on most
+        # firmware, but some return it at the top level; check both.
+        scores = (dto.get("sleepScores") or sleep_data.get("sleepScores")
+                  or dto.get("sleepScore") or sleep_data.get("sleepScore"))
         if isinstance(scores, dict):
             overall = scores.get("overall") or scores.get("qualityScore")
             if isinstance(overall, dict):

backend/app/services/route_matcher.py

@@ -102,49 +102,65 @@ def match_segment_in_activity(
     tol_m: float = 30.0,
 ) -> Optional[float]:
     """
-    Determine whether an activity track traverses a segment's GPS geometry, and if so
-    how long it took. Works even when the activity's overall route differs — only the
-    overlapping stretch matters.
+    Determine whether an activity track traverses a segment's GPS geometry in the
+    segment's own direction, and if so how long the fastest such traversal took.
+    Works even when the activity's overall route differs — only the overlapping
+    stretch matters.
 
     seg_coords: [(lat, lon), ...] segment geometry (start → end).
     act_coords: [(lat, lon), ...] activity track, in time order.
     act_times:  parallel list of datetimes for act_coords.
 
-    Strategy: anchor on the activity point nearest the segment start, then the nearest
-    point (at/after it) to the segment end, then verify a few intermediate segment
-    points are each passed within tolerance between those anchors. Returns the time
-    between the start and end anchors, or None if the activity doesn't follow the segment.
+    Strategy: for every pass of the activity near the segment START, walk forward
+    accumulating path length; accept the traversal only if the activity reaches the
+    segment END after covering roughly the segment's own length (so an out-and-back
+    route can't match an early start to a late finish), and the intermediate segment
+    points are passed in order. Returns the shortest valid traversal time, or None.
     """
     n = len(act_coords)
-    if n < 2 or len(seg_coords) < 2:
+    m = len(seg_coords)
+    if n < 2 or m < 2:
         return None
 
     start_pt, end_pt = seg_coords[0], seg_coords[-1]
-
-    si, sd = None, tol_m
-    for i in range(n):
-        d = haversine_m(act_coords[i], start_pt)
-        if d < sd:
-            sd, si = d, i
-    if si is None:
+    seg_len = sum(haversine_m(seg_coords[k], seg_coords[k + 1]) for k in range(m - 1))
+    if seg_len <= 0:
         return None
 
-    ei, ed = None, tol_m
+    near_start = lambda i: haversine_m(act_coords[i], start_pt) <= tol_m
+
+    # One candidate entry per pass through the start region (first point of each run).
+    entries = [i for i in range(n) if near_start(i) and (i == 0 or not near_start(i - 1))]
+
+    best = None
+    for si in entries:
+        path = 0.0
+        ei = None
         for i in range(si + 1, n):
-        d = haversine_m(act_coords[i], end_pt)
-        if d < ed:
-            ed, ei = d, i
-    if ei is None or ei <= si:
-        return None
+            path += haversine_m(act_coords[i - 1], act_coords[i])
+            if path > seg_len * 1.5:          # wandered too far without finishing → wrong pass/direction
+                break
+            if path >= seg_len * 0.6 and haversine_m(act_coords[i], end_pt) <= tol_m:
+                ei = i
+                break
+        if ei is None:
+            continue
 
-    # Verify the activity actually follows the segment shape between the anchors.
+        # Confirm the activity follows the segment shape in order between the anchors.
+        ok = True
         for frac in (0.25, 0.5, 0.75):
-        sp = seg_coords[int(frac * (len(seg_coords) - 1))]
-        if not any(haversine_m(act_coords[i], sp) <= tol_m for i in range(si, ei + 1)):
-            return None
+            sp = seg_coords[int(frac * (m - 1))]
+            if not any(haversine_m(act_coords[k], sp) <= tol_m for k in range(si, ei + 1)):
+                ok = False
+                break
+        if not ok:
+            continue
 
         dur = (act_times[ei] - act_times[si]).total_seconds()
-    return dur if dur > 0 else None
+        if dur > 0 and (best is None or dur < best):
+            best = dur
+
+    return best
 
 
 def find_best_split_time(

frontend/src/pages/DashboardPage.jsx

@@ -152,7 +152,7 @@ export default function DashboardPage() {
     const rows = [...(recentHealth || [])].sort((a, b) => new Date(b.date) - new Date(a.date))
     const pick = f => rows.find(d => d[f] != null)?.[f] ?? null
     return {
-      date: rows[0]?.date ?? null,
+      date: rows[0]?.date ? rows[0].date.slice(0, 10) : null,   // intraday endpoint wants YYYY-MM-DD
       resting_hr: pick('resting_hr'),
       sleep_duration_s: pick('sleep_duration_s'),
       hrv_nightly_avg: pick('hrv_nightly_avg'),