diff --git a/config.yaml b/config.yaml
index 437f5723..013af9fc 100644
--- a/config.yaml
+++ b/config.yaml
@@ -1,5 +1,5 @@
 # Valid database table names
-SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, fitbit_data, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, screen]
+SENSORS: [applications_crashes, applications_foreground, applications_notifications, battery, bluetooth, calls, locations, messages, plugin_ambient_noise, plugin_device_usage, plugin_google_activity_recognition, screen]
 
 FITBIT_TABLE: [fitbit_data]
 FITBIT_SENSORS: [heartrate, steps, sleep]
@@ -71,7 +71,8 @@ BLUETOOTH:
 
 GOOGLE_ACTIVITY_RECOGNITION:
   DAY_SEGMENTS: *day_segments
-  METRICS: ['count','most_common_activity','number_unique_activities','activity_change_count']
+  METRICS: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle']
+  
 
 BATTERY:
   DAY_SEGMENTS: *day_segments
diff --git a/src/features/google_activity_recognition.py b/src/features/google_activity_recognition.py
index bdfac29e..52a95e47 100644
--- a/src/features/google_activity_recognition.py
+++ b/src/features/google_activity_recognition.py
@@ -4,18 +4,17 @@ import scipy.stats as stats
 from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
 
 day_segment = snakemake.params["segment"]
+metrics = snakemake.params["metrics"]
 
 #Read csv into a pandas dataframe
 data = pd.read_csv(snakemake.input['gar_events'],parse_dates=['local_date_time'])
 ar_deltas = pd.read_csv(snakemake.input['gar_deltas'],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
-columns = ['count','most_common_activity','count_unique_activities','activity_change_count','sumstationary','summobile','sumvehicle']
-columns = list("ar_" + str(day_segment) + "_" + column for column in columns)
-
+columns = list("ar_" + str(day_segment) + "_" + column for column in metrics)
 
 if data.empty:
     finalDataset = pd.DataFrame(columns = columns)
 else:
-
+    finalDataset = pd.DataFrame()
     ar_deltas = splitOvernightEpisodes(ar_deltas, [],['activity'])
 
     if day_segment != "daily":
@@ -31,31 +30,34 @@ else:
     if resampledData.empty:
         finalDataset = pd.DataFrame(columns = columns)
     else:
-        count = resampledData['activity_type'].resample('D').count()
+        #Finding the count of samples of the day
+        if("count" in metrics):
+            finalDataset["ar_" + str(day_segment) + "_count"] = resampledData['activity_type'].resample('D').count()
 
         #Finding most common activity of the day
-        mostCommonActivity = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
+        if("mostcommonactivity" in metrics):
+            finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x:stats.mode(x)[0])
 
         #finding different number of activities during a day
-        uniqueActivities = resampledData['activity_type'].resample('D').nunique()
+        if("countuniqueactivities" in metrics):
+            finalDataset["ar_" + str(day_segment) + "_countuniqueactivities"] = resampledData['activity_type'].resample('D').nunique()
         
         #finding Number of times activity changed
-        resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1),inplace=True)
-        resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
-        countChanges = resampledData['different_activity'].resample('D').sum()
-        finalDataset = pd.concat([count, mostCommonActivity, uniqueActivities, countChanges],axis=1)
+        if("activitychangecount" in metrics):
+            resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1))
+            resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
+            finalDataset["ar_" + str(day_segment) + "_activitychangecount"] = resampledData['different_activity'].resample('D').sum()
+
 
         deltas_metrics = {'sumstationary':['still','tilting'], 
                         'summobile':['on_foot','running','on_bicycle'],
                         'sumvehicle':['in_vehicle']}
-    
+        
         for column, activity_labels in deltas_metrics.items():
-            metric = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]  
-                    .groupby(['local_start_date'])['time_diff']  
-                    .agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))  
-            finalDataset = finalDataset.merge(metric,how='outer',left_index=True,right_index=True)
+            if column in metrics:
+                finalDataset["ar_" + str(day_segment) + "_"+str(column)] = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]  
+                        .groupby(['local_start_date'])['time_diff']  
+                        .agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))  
     
-finalDataset.fillna(0,inplace=True)
 finalDataset.index.names = ['local_date']
-finalDataset.columns=columns
 finalDataset.to_csv(snakemake.output[0])
\ No newline at end of file