diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk
index f642670d..1533f72e 100644
--- a/rules/preprocessing.smk
+++ b/rules/preprocessing.smk
@@ -259,7 +259,8 @@ rule extract_event_information_from_esm:
         stage = "extract",
         pid = "{pid}"
     output:
-        "data/raw/ers/{pid}_ers.csv"
+        "data/raw/ers/{pid}_ers.csv",
+        "data/raw/ers/{pid}_stress_event_targets.csv"
     script:
         "../src/features/phone_esm/straw/process_user_event_related_segments.py"
 
diff --git a/src/features/phone_esm/straw/process_user_event_related_segments.py b/src/features/phone_esm/straw/process_user_event_related_segments.py
index fd0e8b6f..c2569a3b 100644
--- a/src/features/phone_esm/straw/process_user_event_related_segments.py
+++ b/src/features/phone_esm/straw/process_user_event_related_segments.py
@@ -30,6 +30,9 @@ def extract_ers_from_file(esm_df, device_id):
     with open('config.yaml', 'r') as stream:
         config = yaml.load(stream, Loader=yaml.FullLoader)
 
+    
+    pd.DataFrame().to_csv(snakemake.output[1]) # Create an empty stress event file either way
+
 
     esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
 
@@ -69,13 +72,19 @@ def extract_ers_from_file(esm_df, device_id):
             extracted_ers["shift"] = extracted_ers["diffs"].apply(lambda x: format_timestamp(x))
 
     elif targets_method == "stress_event":
-        pass
+        
+        pd.DataFrame().to_csv(snakemake.output[1])
+        
+        # TODO: generiranje ERS datoteke za stress_events
         # VV Testiranje različnih povpraševanj za VV
         # print(esm_df[esm_df.questionnaire_id == 87])
         # filter_esm = esm_df[(esm_df.esm_type == 7) & ((esm_df.questionnaire_id == 90.) | (esm_df.questionnaire_id == 91.))][['questionnaire_id', 'esm_user_answer', 'esm_session']]
         # print(filter_esm[filter_esm.esm_user_answer == "1 - Še vedno traja"].shape)
         # print(filter_esm.shape)
         
+        # TODO: generiranje stress_events_targets datoteke (dodaj tudi stolpec s pid) + dodati moraš merge metodo, ki bo združila te datoteke
+        # TODO: na koncu se mora v čistilni skripti ustrezno odstraniti vse targete in prilepiti nove targete zraven ustreznih segmentov (zna se zgoditi, da bodo overlap)
+        
     else:
         raise Exception("Please select correct target method for the event-related segments.")
         extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])