diff --git a/credentials.yaml b/credentials.yaml
index 7b20cd95..3a1c51e0 100644
--- a/credentials.yaml
+++ b/credentials.yaml
@@ -4,4 +4,3 @@ PSQL_STRAW:
   password: kizi-x2yf-mate
   host: 212.235.208.113
   port: 5432
-  
\ No newline at end of file
diff --git a/src/features/entry.py b/src/features/entry.py
index d8c7e843..a13a93de 100644
--- a/src/features/entry.py
+++ b/src/features/entry.py
@@ -1,5 +1,6 @@
 import pandas as pd
 from utils.utils import fetch_provider_features, run_provider_cleaning_script
+from sklearn.preprocessing import StandardScaler
 
 import sys
 
@@ -9,12 +10,7 @@ provider = snakemake.params["provider"]
 provider_key = snakemake.params["provider_key"]
 sensor_key = snakemake.params["sensor_key"]
 
-calc_windows = False
-try:
-    calc_windows = provider["WINDOWS"]["COMPUTE"]
-except KeyError:
-    print("WINDOWS COMPUTE key not found in config.yaml!")
-
+calc_windows = True if (provider.get("WINDOWS", False) and provider["WINDOWS"].get("COMPUTE", False)) else False
 
 if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall":
     # Data cleaning
@@ -26,8 +22,14 @@ else:
 
     if calc_windows:
         window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)
-        second_order_features.to_csv(snakemake.output[0], index=False)
+        
+        # Z-score SO features by columns
+        if provider["WINDOWS"].get("STANDARDIZE_SO_FEATURES", False):
+            second_order_features[second_order_features.columns[4:]] = StandardScaler().fit_transform(second_order_features[second_order_features.columns[4:]])
+        
         window_features.to_csv(snakemake.output[1], index=False)
+        second_order_features.to_csv(snakemake.output[0], index=False)
+
     elif "empatica" in sensor_key: 
         pd.DataFrame().to_csv(snakemake.output[1], index=False)