diff --git a/Snakefile b/Snakefile
index 475f21c4..b06cd351 100644
--- a/Snakefile
+++ b/Snakefile
@@ -294,12 +294,12 @@ for provider in config["EMPATICA_ACCELEROMETER"]["PROVIDERS"].keys():
             suffixes = get_zip_suffixes(pid)
             files_to_compute.extend(expand("data/raw/{pid}/empatica_accelerometer_unzipped_{suffix}.csv", pid=pid, suffix=suffixes))
             files_to_compute.extend(expand("data/raw/{pid}/empatica_accelerometer_raw_{suffix}.csv", pid=pid, suffix=suffixes))
-        # files_to_compute.extend(expand("data/raw/{pid}/empatica_accelerometer_joined.csv", pid=config["PIDS"]))
-        # files_to_compute.extend(expand("data/raw/{pid}/empatica_accelerometer_with_datetime.csv", pid=config["PIDS"]))
-        # files_to_compute.extend(expand("data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["EMPATICA_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
-        # files_to_compute.extend(expand("data/processed/features/{pid}/empatica_accelerometer.csv", pid=config["PIDS"]))
-        # files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
-        # files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
+        files_to_compute.extend(expand("data/raw/{pid}/empatica_accelerometer_joined.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/empatica_accelerometer_with_datetime.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["EMPATICA_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
+        files_to_compute.extend(expand("data/processed/features/{pid}/empatica_accelerometer.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
+        files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
 
 for provider in config["EMPATICA_HEARTRATE"]["PROVIDERS"].keys():
     if config["EMPATICA_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]:
diff --git a/config.yaml b/config.yaml
index 4fd977c2..ca830600 100644
--- a/config.yaml
+++ b/config.yaml
@@ -427,8 +427,8 @@ EMPATICA_ACCELEROMETER:
   TABLE: acc
   PROVIDERS:
     DBDP:
-      COMPUTE: True
-      FEATURES: []
+      COMPUTE: False
+      FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
       SRC_FOLDER: "dbdp" # inside src/features/empatica_accelerometer
       SRC_LANGUAGE: "python"
 
diff --git a/src/features/empatica_accelerometer/dbdp/main.py b/src/features/empatica_accelerometer/dbdp/main.py
index 82da2f3a..e95636c4 100644
--- a/src/features/empatica_accelerometer/dbdp/main.py
+++ b/src/features/empatica_accelerometer/dbdp/main.py
@@ -3,19 +3,34 @@ import numpy as np
 
 def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
 
-    sensor_data = pd.read_csv(sensor_data_files["sensor_data"])
+    acc_data = pd.read_csv(sensor_data_files["sensor_data"])
     requested_features = provider["FEATURES"]
     # name of the features this function can compute
-    base_features_names = [] # ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
+    base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
     # the subset of requested features this function can compute
     features_to_compute = list(set(requested_features) & set(base_features_names))
 
-    features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
-    if not sensor_data.empty:
-        sensor_data = filter_data_by_segment(sensor_data, time_segment)
+    acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
+    if not acc_data.empty:
+        acc_data = filter_data_by_segment(acc_data, time_segment)
         
-        if not sensor_data.empty:
-            features = pd.DataFrame()
+        if not acc_data.empty:
+            acc_features = pd.DataFrame()
+            # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
+            magnitude = acc_data.apply(lambda row: np.sqrt(row["x"] ** 2 + row["y"] ** 2 + row["z"] ** 2), axis=1)
+            acc_data = acc_data.assign(magnitude = magnitude.values)
             
+            if "maxmagnitude" in features_to_compute:
+                acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max()
+            if "minmagnitude" in features_to_compute:
+                acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min()
+            if "avgmagnitude" in features_to_compute:
+                acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean()
+            if "medianmagnitude" in features_to_compute:
+                acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median()
+            if "stdmagnitude" in features_to_compute:
+                acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std()
+            
+            acc_features = acc_features.reset_index()
 
-    return features
\ No newline at end of file
+    return acc_features
\ No newline at end of file