diff --git a/pyproject.toml b/pyproject.toml
index 0ddc0be..8b8b28f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "jabs-postprocess"
-version = "0.5.0"
+version = "0.5.1"
 description = "A python library for JABS postprocessing utilities."
 readme = "README.md"
 license = "LicenseRef-PLATFORM-LICENSE-AGREEMENT-FOR-NON-COMMERCIAL-USE"
diff --git a/src/jabs_postprocess/heuristic_classifiers/locomotion_corner.yaml b/src/jabs_postprocess/heuristic_classifiers/locomotion_corner.yaml
new file mode 100644
index 0000000..a117371
--- /dev/null
+++ b/src/jabs_postprocess/heuristic_classifiers/locomotion_corner.yaml
@@ -0,0 +1,43 @@
+# Locomotion in corner
+# Within 20% distance of any 2 walls
+# Mouse is moving > 5cm/s
+# This is really slow for a mouse, but essentially just removes noise from micromovements
+behavior: 
+  Locomotion In Corner
+
+interpolate:
+  5
+
+stitch:
+  5
+
+min_bout:
+  15
+
+definition:
+  and:
+    - minimum:
+      - 2
+      - less than:
+        - wall_distances/wall_0
+        - divide:
+          - avg_wall_length
+          - 5
+      - less than:
+        - wall_distances/wall_1
+        - divide:
+          - avg_wall_length
+          - 5
+      - less than:
+        - wall_distances/wall_2
+        - divide:
+          - avg_wall_length
+          - 5
+      - less than:
+        - wall_distances/wall_3
+        - divide:
+          - avg_wall_length
+          - 5
+    - greater than:
+      - features/per_frame/centroid_velocity_mag centroid_velocity_mag
+      - 5.0
diff --git a/src/jabs_postprocess/heuristic_classifiers/locomotion_periphery.yaml b/src/jabs_postprocess/heuristic_classifiers/locomotion_periphery.yaml
new file mode 100644
index 0000000..eadb49c
--- /dev/null
+++ b/src/jabs_postprocess/heuristic_classifiers/locomotion_periphery.yaml
@@ -0,0 +1,26 @@
+# Locomotion in periphery
+# Within 20% distance of any wall
+# Mouse is moving > 5cm/s
+# This is really slow for a mouse, but essentially just removes noise from micromovements
+behavior: 
+  Locomotion In Periphery
+
+interpolate:
+  5
+
+stitch:
+  5
+
+min_bout:
+  15
+
+definition:
+  and:
+    - less than:
+      - features/per_frame/corner_distances distance to wall
+      - divide:
+        - avg_wall_length
+        - 5
+    - greater than:
+      - features/per_frame/centroid_velocity_mag centroid_velocity_mag
+      - 5.0
diff --git a/src/jabs_postprocess/utils/project_utils.py b/src/jabs_postprocess/utils/project_utils.py
index e7b77d2..38d2fbe 100644
--- a/src/jabs_postprocess/utils/project_utils.py
+++ b/src/jabs_postprocess/utils/project_utils.py
@@ -509,6 +509,8 @@ def __init__(self, settings: ClassifierSettings, data: pd.DataFrame):
             "exp_prefix",
             "time",
             "distance",
+            "distance_threshold",
+            "distance_seg",
             "closest_id",
             "closest_lixit",
             "closest_corner",
@@ -677,6 +679,8 @@ def __init__(self, settings: ClassifierSettings, data: pd.DataFrame):
             "exp_prefix",
             "time",
             "distance",
+            "distance_threshold",
+            "distance_seg",
             "closest_id",
             "closest_lixit",
             "closest_corner",
@@ -770,6 +774,16 @@ def add_bout_features(self, feature_file: Path):
                 "features/per_frame/centroid_velocity_mag centroid_velocity_mag",
                 lambda x: np.nansum(x, initial=0) / 30,
             ),
+            FeatureInEvent(
+                "distance_threshold",
+                "features/per_frame/centroid_velocity_mag centroid_velocity_mag",
+                lambda x: np.nansum(x[x > 5], initial=0) / 30,
+            ),
+            FeatureInEvent(
+                "distance_seg",
+                "features/per_frame/shape_descriptor centroid_speed",
+                lambda x: np.nansum(x, initial=0) / 30,
+            ),
             FeatureInEvent("closest_id", "closest_identities", np.median),
             FeatureInEvent("closest_lixit", "closest_lixit", np.median),
             FeatureInEvent("closest_corner", "closest_corners", np.median),
@@ -1003,6 +1017,14 @@ def bouts_to_bins(
                 bins_to_summarize["calc_dist"] = (
                     bins_to_summarize["distance"] * bins_to_summarize["percent_bout"]
                 )
+                bins_to_summarize["calc_dist_threshold"] = (
+                    bins_to_summarize["distance_threshold"]
+                    * bins_to_summarize["percent_bout"]
+                )
+                bins_to_summarize["calc_dist_seg"] = (
+                    bins_to_summarize["distance_seg"]
+                    * bins_to_summarize["percent_bout"]
+                )
             else:
                 pass
             pd.options.mode.chained_assignment = "warn"
@@ -1024,12 +1046,10 @@ def bouts_to_bins(
             # We use a weighted statistic definitions here
             # Weights are the proportion of bout contained in the bin (percent_bout)
             if results["bout_behavior"] > 0:
-                results["avg_bout_duration"] = (
-                    np.sum(
-                        behavior_bins["duration"].values
-                        * behavior_bins["percent_bout"].values
-                    )
-                    / results["bout_behavior"]
+                results["avg_bout_duration"] = np.average(
+                    behavior_bins["duration"].values
+                    / behavior_bins["percent_bout"].values,
+                    weights=behavior_bins["percent_bout"].values,
                 )
                 results["latency_to_first_prediction"] = behavior_bins["start"].min()
                 results["latency_to_last_prediction"] = (
@@ -1072,6 +1092,12 @@ def bouts_to_bins(
                 results["behavior_dist"] = bins_to_summarize.loc[
                     bins_to_summarize["is_behavior"] == 1, "calc_dist"
                 ].sum()
+                results["behavior_dist_threshold"] = bins_to_summarize.loc[
+                    bins_to_summarize["is_behavior"] == 1, "calc_dist_threshold"
+                ].sum()
+                results["behavior_dist_seg"] = bins_to_summarize.loc[
+                    bins_to_summarize["is_behavior"] == 1, "calc_dist_seg"
+                ].sum()
             results_df_list.append(pd.DataFrame(results))
 
         # Remove an non-informative rows
@@ -1153,6 +1179,8 @@ def __init__(self, settings: ClassifierSettings, data: pd.DataFrame):
             "time",
             "not_behavior_dist",
             "behavior_dist",
+            "behavior_dist_threshold",
+            "behavior_dist_seg",
             "avg_bout_duration",
             "_stats_sample_count",
             "bout_duration_std",
diff --git a/tests/utils/test_project_utils.py b/tests/utils/test_project_utils.py
index 9009370..af6ce54 100644
--- a/tests/utils/test_project_utils.py
+++ b/tests/utils/test_project_utils.py
@@ -759,15 +759,15 @@ def test_bouts_to_bins_split_bout_weighted_statistics(self):
         # Both bins should have:
         # - bout_behavior = 0.5 (half the bout counted)
         # - time_behavior = 300 (half the frames)
-        # - avg_bout_duration = 300 (the actual duration in each bin)
+        # - avg_bout_duration = 600 (duration remains same, average of 1 sample)
         # - variance = NaN (only one bout, even if split)
         assert len(result) == 2
 
         for i in range(2):
             assert abs(result.iloc[i]["bout_behavior"] - 0.5) < 0.01
             assert result.iloc[i]["time_behavior"] == 300
-            # When a bout is split, the avg_bout_duration is the fractional duration
-            assert abs(result.iloc[i]["avg_bout_duration"] - 300) < 0.1
+            # When a bout is split, the avg_bout_duration is still the total duration
+            assert abs(result.iloc[i]["avg_bout_duration"] - 600) < 0.1
             assert pd.isna(result.iloc[i]["bout_duration_var"])
 
     def test_bouts_to_bins_multiple_split_bouts_variance(self):
diff --git a/uv.lock b/uv.lock
index 3d517ae..a774e8f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -340,7 +340,7 @@ wheels = [
 
 [[package]]
 name = "jabs-postprocess"
-version = "0.5.0"
+version = "0.5.1"
 source = { editable = "." }
 dependencies = [
     { name = "black" },
diff --git a/vm/jabs-postprocess.def b/vm/jabs-postprocess.def
index be1aa11..7a58cd4 100644
--- a/vm/jabs-postprocess.def
+++ b/vm/jabs-postprocess.def
@@ -62,5 +62,5 @@ From: ghcr.io/astral-sh/uv:python3.10-bookworm-slim
 
 %labels
 	Author "The Kumar Lab"
-	Version "0.1.0"
+	Version "0.5.1"
 	Description "JABS post-processing container with Python 3.10"