diff --git a/Snakefile b/Snakefile
index d849a3f4..e98061fe 100644
--- a/Snakefile
+++ b/Snakefile
@@ -44,6 +44,9 @@ rule all:
         expand("data/raw/{pid}/fitbit_{fitbit_sensor}_with_datetime.csv",
                             pid=config["PIDS"],
                             fitbit_sensor=config["FITBIT_SENSORS"]),
+        expand("data/processed/{pid}/fitbit_step_{day_segment}.csv",
+                            pid = config["PIDS"],
+                            day_segment = config["STEP"]["DAY_SEGMENTS"]),
         # Reports
         expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"]),
diff --git a/config.yaml b/config.yaml
index 013af9fc..377afd85 100644
--- a/config.yaml
+++ b/config.yaml
@@ -91,3 +91,11 @@ LIGHT:
 ACCELEROMETER:
   DAY_SEGMENTS: *day_segments
   METRICS: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude", "ratioexertionalactivityepisodes", "sumexertionalactivityepisodes", "longestexertionalactivityepisode", "longestnonexertionalactivityepisode", "countexertionalactivityepisodes", "countnonexertionalactivityepisodes"]
+
+STEP:
+  DAY_SEGMENTS: *day_segments
+  METRICS:
+    ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"]
+    SEDENTARY_BOUT: ["countsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout"]
+    ACTIVE_BOUT: ["countactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"]
+  THRESHOLD_ACTIVE_BOUT: 10 # steps
\ No newline at end of file
diff --git a/rules/features.snakefile b/rules/features.snakefile
index c40e3cbc..b10d8ace 100644
--- a/rules/features.snakefile
+++ b/rules/features.snakefile
@@ -130,3 +130,17 @@ rule accelerometer_metrics:
         "data/processed/{pid}/accelerometer_{day_segment}.csv"
     script:
         "../src/features/accelerometer_metrics.py"
+
+rule fitbit_step_metrics:
+    input:
+        steps_data = "data/raw/{pid}/fitbit_steps_with_datetime.csv",
+    params:
+        day_segment = "{day_segment}",
+        metrics_all_steps = config["STEP"]["METRICS"]["ALL_STEPS"],
+        metrics_sedentary_bout = config["STEP"]["METRICS"]["SEDENTARY_BOUT"],
+        metrics_active_bout = config["STEP"]["METRICS"]["ACTIVE_BOUT"],
+        threshold_active_bout = config["STEP"]["THRESHOLD_ACTIVE_BOUT"]
+    output:
+        "data/processed/{pid}/fitbit_step_{day_segment}.csv"
+    script:
+        "../src/features/fitbit_step_metrics.py"
diff --git a/src/features/fitbit_step_metrics.py b/src/features/fitbit_step_metrics.py
new file mode 100644
index 00000000..f436b53d
--- /dev/null
+++ b/src/features/fitbit_step_metrics.py
@@ -0,0 +1,97 @@
+import pandas as pd
+import numpy as np
+import datetime as dt
+from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
+
+day_segment = snakemake.params["day_segment"]
+all_steps = snakemake.params["metrics_all_steps"]
+sedentary_bout = snakemake.params["metrics_sedentary_bout"]
+active_bout = snakemake.params["metrics_active_bout"]
+threshold_active_bout = snakemake.params['threshold_active_bout']
+
+#Read csv into a pandas dataframe
+data = pd.read_csv(snakemake.input['steps_data'],parse_dates=['local_date_time'])
+columns = list("step_" + str(day_segment) + "_" + column for column in (all_steps + sedentary_bout + active_bout))
+
+if (day_segment != 'daily'):
+    data = data.loc[data['local_day_segment'] == str(day_segment)]
+    
+if data.empty:
+    finalDataset = pd.DataFrame(columns = columns)
+else:
+    finalDataset = pd.DataFrame()
+
+    #Preprocessing:
+    data.local_date_time = pd.to_datetime(data.local_date_time)
+    resampledData = data.set_index(data.local_date_time)
+    resampledData.index.names = ['datetime']
+
+    resampledData['time_diff_minutes'] = resampledData['local_date_time'].diff().fillna(pd.Timedelta(seconds=0)).dt.total_seconds().div(60).astype(int)
+
+    #Sedentary Bout when you have less than 10 steps in a minute
+    #Active Bout when you have greater or equal to 10 steps in a minute
+    resampledData['active_sedentary'] = np.where(resampledData['steps']<int(threshold_active_bout),'sedentary','active')
+    
+    activeData = resampledData[resampledData['active_sedentary']=="active"]
+    sedentaryData = resampledData[resampledData['active_sedentary']=="sedentary"]
+
+    #Time Calculations of sedentary/active bouts:
+    resampledData['active_sedentary_groups'] = (resampledData.active_sedentary != resampledData.active_sedentary.shift()).cumsum().values
+
+    #Get the total minutes for each episode
+    minutesGroupedBy = resampledData.groupby(['local_date','active_sedentary','active_sedentary_groups'])['time_diff_minutes'].sum()
+    
+    #Get Stats for all episodes in terms of minutes
+    statsMinutes = minutesGroupedBy.groupby(['local_date','active_sedentary']).agg([max,min,np.mean,np.std])
+    mux = pd.MultiIndex.from_product([statsMinutes.index.levels[0], statsMinutes.index.levels[1]],names=['local_date','active_sedentary'])
+    statsMinutes = statsMinutes.reindex(mux, fill_value=None).reset_index()
+    statsMinutes.set_index('local_date',inplace = True)
+    
+    #Descriptive Statistics Features:
+    if("sumallsteps" in all_steps):
+        finalDataset["step_" + str(day_segment) + "_sumallsteps"] = resampledData['steps'].resample('D').sum()
+    
+    if("maxallsteps" in all_steps):
+        finalDataset["step_" + str(day_segment) + "_maxallsteps"] = resampledData['steps'].resample('D').max()
+
+    if("minallsteps" in all_steps):
+        finalDataset["step_" + str(day_segment) + "_minallsteps"] = resampledData['steps'].resample('D').min()
+    
+    if("avgallsteps" in all_steps):
+        finalDataset["step_" + str(day_segment) + "_avgallsteps"] = resampledData['steps'].resample('D').mean()
+    
+    if("stdallsteps" in all_steps):
+        finalDataset["step_" + str(day_segment) + "_stdallsteps"] = resampledData['steps'].resample('D').std()
+    
+    if("countsedentarybout" in sedentary_bout):
+        finalDataset["step_" + str(day_segment) + "_countsedentarybout"] = sedentaryData['active_sedentary'].resample('D').count()
+
+    if("countactivebout" in active_bout):
+        finalDataset["step_" + str(day_segment) + "_countactivebout"] = activeData['active_sedentary'].resample('D').count()
+
+    if("maxdurationsedentarybout" in sedentary_bout):
+        finalDataset["step_" + str(day_segment) + "_maxdurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['max']
+    
+    if("mindurationsedentarybout" in sedentary_bout):
+        finalDataset["step_" + str(day_segment) + "_mindurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['min']
+
+    if("avgdurationsedentarybout" in sedentary_bout):
+        finalDataset["step_" + str(day_segment) + "_avgdurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['mean']
+
+    if("stddurationsedentarybout" in sedentary_bout):
+        finalDataset["step_" + str(day_segment) + "_stddurationsedentarybout"] = statsMinutes[statsMinutes['active_sedentary']=='sedentary']['std']
+    
+    if("maxdurationactivebout" in active_bout):
+        finalDataset["step_" + str(day_segment) + "_maxdurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['max']
+    
+    if("mindurationactivebout" in active_bout):
+        finalDataset["step_" + str(day_segment) + "_mindurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['min']
+    
+    if("avgdurationactivebout" in active_bout):
+        finalDataset["step_" + str(day_segment) + "_avgdurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['mean']
+    
+    if("stddurationactivebout" in active_bout):
+        finalDataset["step_" + str(day_segment) + "_stddurationactivebout"] = statsMinutes[statsMinutes['active_sedentary']== 'active']['std']
+
+finalDataset.index.names = ['local_date']
+finalDataset.to_csv(snakemake.output[0])