diff --git a/my/body/exercise/cardio.py b/my/body/exercise/cardio.py
new file mode 100644
index 0000000..2c6a7e5
--- /dev/null
+++ b/my/body/exercise/cardio.py
@@ -0,0 +1,50 @@
+'''
+Cardio data, filtered from Endomondo and inferred from other data sources
+'''
+from ...core.pandas import check_dataframe as cdf
+
+import pandas as pd # type: ignore
+
+
+CARDIO     = {
+    'Running',
+    'Running, treadmill',
+    'Cross training',
+    'Walking',
+    'Skating',
+    'Spinning',
+    'Skiing',
+    'Table tennis',
+    'Rope jumping',
+}
+# todo if it has HR data, take it into the account??
+NOT_CARDIO = {
+    'Other',
+}
+
+
+@cdf
+def endomondo_cardio() -> pd.DataFrame:
+    assert len(CARDIO.intersection(NOT_CARDIO)) == 0, (CARDIO, NOT_CARDIO)
+
+    from ..endomondo import dataframe as EDF
+    df = EDF()
+
+    # not sure...
+    # df = df[df['heart_rate_avg'].notna()]
+
+    is_cardio  = df['sport'].isin(CARDIO)
+    not_cardio = df['sport'].isin(NOT_CARDIO)
+    neither    = ~is_cardio & ~not_cardio
+    # if neither -- count, but warn? or show error?
+
+    # todo error about the rest??
+    # todo append errors?
+    df.loc[neither, 'error'] = 'Unexpected exercise type, please mark as cardio or non-cardio'
+    df = df[is_cardio | neither]
+
+    return df
+
+
+def dataframe():
+    return endomondo_cardio()
diff --git a/my/body/exercise.py b/my/body/exercise/cross_trainer.py
similarity index 93%
rename from my/body/exercise.py
rename to my/body/exercise/cross_trainer.py
index 797fadf..3457412 100644
--- a/my/body/exercise.py
+++ b/my/body/exercise/cross_trainer.py
@@ -1,5 +1,5 @@
 '''
-My exercise data, arbitrated between differen sources (mainly, Endomondo and various manual plaintext notes)
+My cross trainer exercise data, arbitrated between differen sources (mainly, Endomondo and various manual plaintext notes)
 
 This is probably too specific to my needs, so later I will move it away to a personal 'layer'.
 For now it's worth keeping it here as an example and perhaps utility functions might be useful for other HPI modules.
@@ -8,7 +8,7 @@ For now it's worth keeping it here as an example and perhaps utility functions m
 from datetime import datetime, timedelta
 from typing import Optional
 
-from ..core.pandas import check_dataframe as cdf
+from ...core.pandas import check_dataframe as cdf
 
 from my.config import exercise as config
 
@@ -46,7 +46,7 @@ def cross_trainer_data():
     # todo eh. not sure if there is a way of getting around writing code...
     # I guess would be nice to have a means of specifying type in the column? maybe multirow column names??
     # need to look up org-mode standard..
-    from ..core.orgmode import parse_org_datetime
+    from ...core.orgmode import parse_org_datetime
     mappers = {
         'duration': lambda s: parse_mm_ss(s),
         'date'    : lambda s: tzify(parse_org_datetime(s)),
@@ -83,13 +83,13 @@ _DELTA = timedelta(hours=10)
 
 # todo check error handling by introducing typos (e.g. especially dates) in org-mode
 @cdf
-def cross_trainer_dataframe():
+def dataframe():
     '''
     Attaches manually logged data (which Endomondo can't capture) and attaches it to Endomondo
     '''
     import pandas as pd
 
-    from ..endomondo import dataframe as EDF
+    from ...endomondo import dataframe as EDF
     edf = EDF()
     edf = edf[edf['sport'].str.contains('Cross training')]
 
@@ -160,12 +160,12 @@ def cross_trainer_dataframe():
 
 
 def stats():
-    from ..core import stat
-    return stat(cross_trainer_data())
+    from ...core import stat
+    return stat(cross_trainer_data)
 
 
 def compare_manual():
-    df = cross_trainer_dataframe()
+    df = dataframe()
     df = df.set_index('start_time')
 
     df = df[[