Vote on core windows only, skip first/last (recording start/stop transients)

src/phyphox_app_block.py

@@ -206,8 +206,13 @@ def render_phyphox_tab(
             pred_labels = [LABEL_MAP[int(np.argmax(p))] for p in probs_all]
 
             from collections import Counter
-            vote = Counter(pred_labels).most_common(1)[0][0]
-            avg_conf = float(np.mean(np.max(probs_all, axis=1))) * 100
+            # Skip first and last window for the final vote: these are typically
+            # contaminated by recording start/stop transients (person not yet
+            # in full motion, or the gravity filter still warming up).
+            core = probs_all[1:-1] if n_windows > 3 else probs_all
+            core_labels = [LABEL_MAP[int(np.argmax(p))] for p in core]
+            vote = Counter(core_labels).most_common(1)[0][0]
+            avg_conf = float(np.mean(np.max(core, axis=1))) * 100
 
             st.success(f"**{vote}** · {avg_conf:.1f}% avg confidence")
             st.markdown(f"_{EXPLANATIONS[vote]}_")
@@ -217,15 +222,18 @@ def render_phyphox_tab(
                     rows = []
                     for i, (p, label) in enumerate(zip(probs_all, pred_labels)):
                         t_start = i * STEP / FS
+                        is_edge = (i == 0 or i == n_windows - 1) and n_windows > 3
                         rows.append({
                             "Window": i + 1,
                             "Time (s)": f"{t_start:.1f}–{t_start + WINDOW/FS:.1f}",
-                            "Prediction": label,
+                            "Prediction": label + (" *" if is_edge else ""),
                             "Confidence": f"{float(np.max(p))*100:.1f}%",
                         })
                     st.dataframe(pd.DataFrame(rows), use_container_width=True)
+                    if n_windows > 3:
+                        st.caption("* Edge windows excluded from overall vote (recording start/stop transient).")
 
-            mean_probs = probs_all.mean(axis=0)
+            mean_probs = core.mean(axis=0)
             st.markdown("**Average confidence across all classes**")
             st.bar_chart(pd.DataFrame(
                 {"Confidence (%)": [float(mean_probs[i]) * 100 for i in range(6)]},