diff --git a/config.yaml b/config.yaml
index 7e44df20..21b09f81 100644
--- a/config.yaml
+++ b/config.yaml
@@ -90,6 +90,7 @@ DORYAB_LOCATION:
   DBSCAN_EPS: 10 # meters
   DBSCAN_MINSAMPLES: 5
   THRESHOLD_STATIC : 1 # km/h
+  MAXIMUM_GAP_ALLOWED: 300
 
 BLUETOOTH:
   COMPUTE: False
diff --git a/rules/features.snakefile b/rules/features.snakefile
index e8de9167..34dc80a3 100644
--- a/rules/features.snakefile
+++ b/rules/features.snakefile
@@ -129,7 +129,8 @@ rule location_doryab_features:
         day_segment = "{day_segment}",
         dbscan_eps = config["DORYAB_LOCATION"]["DBSCAN_EPS"],
         dbscan_minsamples = config["DORYAB_LOCATION"]["DBSCAN_MINSAMPLES"],
-        threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"]
+        threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"],
+        maximum_gap_allowed = config["DORYAB_LOCATION"]["MAXIMUM_GAP_ALLOWED"]
     output:
         "data/processed/{pid}/location_doryab_{day_segment}.csv"
     script:
diff --git a/src/features/location_doryab/location_base.py b/src/features/location_doryab/location_base.py
index 1a8aee82..e40603ce 100644
--- a/src/features/location_doryab/location_base.py
+++ b/src/features/location_doryab/location_base.py
@@ -4,7 +4,7 @@ from astropy.timeseries import LombScargle
 from sklearn.cluster import DBSCAN
 from math import radians, cos, sin, asin, sqrt
 
-def base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples, threshold_static):
+def base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples, threshold_static, maximum_gap_allowed):
     # name of the features this function can compute
     base_features_names = ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"]
 
@@ -34,7 +34,7 @@ def base_location_features(location_data, day_segment, requested_features, dbsca
             
             preComputedDistanceandSpeed = pd.DataFrame()
             for localDate in location_data['local_date'].unique():
-                distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_date']==localDate],threshold_static)
+                distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_date']==localDate],threshold_static,maximum_gap_allowed)
                 preComputedDistanceandSpeed.loc[localDate,"distance"] = distance.sum()
                 preComputedDistanceandSpeed.loc[localDate,"avgspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].mean()
                 preComputedDistanceandSpeed.loc[localDate,"varspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].var()
@@ -133,7 +133,7 @@ def distance_to_degrees(d):
     d = d / 60
     return d
 
-def get_all_travel_distances_meters_speed(locationData,threshold):
+def get_all_travel_distances_meters_speed(locationData,threshold,maximum_gap_allowed):
     
     lat_lon_temp = pd.DataFrame()
 
@@ -146,7 +146,7 @@ def get_all_travel_distances_meters_speed(locationData,threshold):
     lat_lon_temp['time_diff'] = lat_lon_temp['time_after'] - lat_lon_temp['time_before']
     lat_lon_temp['timeInSeconds'] = lat_lon_temp['time_diff'].apply(lambda x: x.total_seconds())
 
-    lat_lon_temp = lat_lon_temp[lat_lon_temp['timeInSeconds'] <= 300]
+    lat_lon_temp = lat_lon_temp[lat_lon_temp['timeInSeconds'] <= maximum_gap_allowed]
 
     if lat_lon_temp.empty:
         return pd.Series(), pd.DataFrame({"speed": [], "speedTag": []})
diff --git a/src/features/location_doryab_features.py b/src/features/location_doryab_features.py
index 2a78e98e..a053a8d8 100644
--- a/src/features/location_doryab_features.py
+++ b/src/features/location_doryab_features.py
@@ -8,8 +8,9 @@ location_features = pd.DataFrame(columns=["local_date"])
 dbscan_eps = snakemake.params["dbscan_eps"]
 dbscan_minsamples = snakemake.params["dbscan_minsamples"]
 threshold_static = snakemake.params["threshold_static"]
+maximum_gap_allowed = snakemake.params["maximum_gap_allowed"]
 
-location_features = location_features.merge(base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static), on="local_date", how="outer")
+location_features = location_features.merge(base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed), on="local_date", how="outer")
 
 assert len(requested_features) + 1 == location_features.shape[1], "The number of features in the output dataframe (=" + str(location_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your location feature extraction functions"