diff --git a/config.yaml b/config.yaml
index 77088c26..2a38e75e 100644
--- a/config.yaml
+++ b/config.yaml
@@ -240,7 +240,7 @@ PHONE_ESM:
     STRAW:
       COMPUTE: True
       SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
-      SRC_SCRIPT: src/features/phone_esm/rapids/main.py
+      SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
 # See https://www.rapids.science/latest/features/phone-keyboard/
 PHONE_KEYBOARD:
diff --git a/src/features/phone_esm/straw/main.py b/src/features/phone_esm/straw/main.py
index 8ed3db5d..81992be0 100644
--- a/src/features/phone_esm/straw/main.py
+++ b/src/features/phone_esm/straw/main.py
@@ -2,6 +2,16 @@ import pandas as pd
 
 
 def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    # empty for now
-    your_features_df = pd.DataFrame()
-    return(your_features_df)
+    esm_data = pd.read_csv(sensor_data_files["sensor_data"])
+    requested_features = provider["FEATURES"]
+    # name of the features this function can compute
+    base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
+    # the subset of requested features this function can compute
+    features_to_compute = list(set(requested_features) & set(base_features_names))
+    esm_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
+
+    esm_data = filter_data_by_segment(esm_data, time_segment)
+    esm_features["mean"] = esm_data.groupby(["local_segment"])["esm_user_score"].mean()
+    #TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
+    esm_features = esm_features.reset_index()
+    return esm_features