Modify the Accelerometer ‘Metrics’ to Accelerometer ‘Features’
Co-authored-by: Meng Li <AnnieLM1996@gmail.com> Co-authored-by: JulioV <juliovhz@gmail.com>pull/95/head
Mingze Cao
parent
9d27431e3b
commit
6045df494d
|
|
@ -98,7 +98,7 @@ LIGHT:
|
|||
|
||||
ACCELEROMETER:
|
||||
DAY_SEGMENTS: *day_segments
|
||||
METRICS: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude", "ratioexertionalactivityepisodes", "sumexertionalactivityepisodes", "longestexertionalactivityepisode", "longestnonexertionalactivityepisode", "countexertionalactivityepisodes", "countnonexertionalactivityepisodes"]
|
||||
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude", "ratioexertionalactivityepisodes", "sumexertionalactivityepisodes", "longestexertionalactivityepisode", "longestnonexertionalactivityepisode", "countexertionalactivityepisodes", "countnonexertionalactivityepisodes"]
|
||||
|
||||
APPLICATIONS_FOREGROUND:
|
||||
DAY_SEGMENTS: *day_segments
|
||||
|
|
|
|||
|
|
@ -365,7 +365,7 @@ See `Accelerometer Config Code`_
|
|||
|
||||
.. - Apply readable datetime to Accelerometer dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
|
||||
|
||||
- Extract Calls Metrics
|
||||
- Extract Accelerometer Features
|
||||
|
||||
| ``expand("data/processed/{pid}/accelerometer_{day_segment}.csv",``
|
||||
| ``pid=config["PIDS"],``
|
||||
|
|
@ -381,9 +381,9 @@ See `Accelerometer Config Code`_
|
|||
|
||||
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
|
||||
|
||||
- **Rule:** ``rules/features.snakefile/accelerometer_metrics`` - See the accelerometer_metrics_ rule.
|
||||
- **Rule:** ``rules/features.snakefile/accelerometer_features`` - See the accelerometer_features_ rule.
|
||||
|
||||
- **Script:** ``src/features/accelerometer_metrics.py`` - See the accelerometer_metrics.py_ script.
|
||||
- **Script:** ``src/features/accelerometer_features.py`` - See the accelerometer_features.py_ script.
|
||||
|
||||
|
||||
.. _Accelerometer-parameters:
|
||||
|
|
@ -394,14 +394,14 @@ See `Accelerometer Config Code`_
|
|||
Name Description
|
||||
============ ===================
|
||||
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
|
||||
metrics The different measures that can be retrieved from the dataset. See :ref:`Available Accelerometer Metrics <accelerometer-available-metrics>` Table below
|
||||
features The different measures that can be retrieved from the dataset. See :ref:`Available Accelerometer Features <accelerometer-available-features>` Table below
|
||||
============ ===================
|
||||
|
||||
.. _accelerometer-available-metrics:
|
||||
.. _accelerometer-available-features:
|
||||
|
||||
**Available Accelerometer Metrics**
|
||||
**Available Accelerometer Features**
|
||||
|
||||
The following table shows a list of the available metrics the accelerometer sensor data for a particular ``day_segment``.
|
||||
The following table shows a list of the available features the accelerometer sensor data for a particular ``day_segment``.
|
||||
|
||||
==================================== ============== =============
|
||||
Name Units Description
|
||||
|
|
|
|||
|
|
@ -122,16 +122,16 @@ rule light_metrics:
|
|||
script:
|
||||
"../src/features/light_metrics.py"
|
||||
|
||||
rule accelerometer_metrics:
|
||||
rule accelerometer_features:
|
||||
input:
|
||||
"data/raw/{pid}/accelerometer_with_datetime.csv",
|
||||
params:
|
||||
day_segment = "{day_segment}",
|
||||
metrics = config["ACCELEROMETER"]["METRICS"],
|
||||
features = config["ACCELEROMETER"]["FEATURES"],
|
||||
output:
|
||||
"data/processed/{pid}/accelerometer_{day_segment}.csv"
|
||||
script:
|
||||
"../src/features/accelerometer_metrics.py"
|
||||
"../src/features/accelerometer_features.py"
|
||||
|
||||
rule applications_foreground_metrics:
|
||||
input:
|
||||
|
|
|
|||
|
|
@ -0,0 +1,87 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
def getActivityEpisodes(acc_minute, activity_type):
|
||||
col_name = ["nonexertional_episodes", "exertional_episodes"][activity_type]
|
||||
|
||||
# rebuild local date time for resampling
|
||||
acc_minute["local_datetime"] = pd.to_datetime(acc_minute["local_date"].dt.strftime("%Y-%m-%d") + \
|
||||
" " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00")
|
||||
# resample the data into 1 minute bins
|
||||
resampled_acc_minute = pd.DataFrame(acc_minute.resample("1T", on="local_datetime")["isexertionalactivity"].sum())
|
||||
|
||||
if activity_type == 0:
|
||||
resampled_acc_minute["isexertionalactivity"] = resampled_acc_minute["isexertionalactivity"] * (-1) + 1
|
||||
|
||||
# get the longest episode of exertional/non-exertional activity given as consecutive one minute periods
|
||||
resampled_acc_minute['consecutive'] = resampled_acc_minute["isexertionalactivity"].groupby((resampled_acc_minute["isexertionalactivity"] != resampled_acc_minute["isexertionalactivity"].shift()).cumsum()).transform('size') * resampled_acc_minute["isexertionalactivity"]
|
||||
longest_activity_episodes = resampled_acc_minute.groupby(pd.Grouper(freq='D'))[["consecutive"]].max().rename(columns = {"consecutive": col_name})
|
||||
|
||||
# get the count of exertional/non-exertional activity episodes
|
||||
resampled_acc_minute_shift = resampled_acc_minute.loc[resampled_acc_minute["consecutive"].shift() != resampled_acc_minute["consecutive"]]
|
||||
count_activity_episodes = resampled_acc_minute_shift.groupby(pd.Grouper(freq='D'))[["consecutive"]].apply(lambda x: np.count_nonzero(x)).to_frame(name = col_name)
|
||||
|
||||
return longest_activity_episodes, count_activity_episodes
|
||||
|
||||
def dropRowsWithCertainThreshold(data, threshold):
|
||||
data_grouped = data.groupby(["local_date", "local_hour", "local_minute"]).count()
|
||||
drop_dates = data_grouped[data_grouped["timestamp"] == threshold].index
|
||||
data.set_index(["local_date", "local_hour", "local_minute"], inplace = True)
|
||||
if not drop_dates.empty:
|
||||
data.drop(drop_dates, axis = 0, inplace = True)
|
||||
return data.reset_index()
|
||||
|
||||
|
||||
acc_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
|
||||
day_segment = snakemake.params["day_segment"]
|
||||
features = snakemake.params["features"]
|
||||
|
||||
acc_features = pd.DataFrame(columns=["local_date"] + ["acc_" + day_segment + "_" + x for x in features])
|
||||
if not acc_data.empty:
|
||||
if day_segment != "daily":
|
||||
acc_data = acc_data[acc_data["local_day_segment"] == day_segment]
|
||||
if not acc_data.empty:
|
||||
acc_features = pd.DataFrame()
|
||||
# get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)
|
||||
acc_data["magnitude"] = (acc_data["double_values_0"] ** 2 + acc_data["double_values_1"] ** 2 + acc_data["double_values_2"] ** 2).apply(np.sqrt)
|
||||
if "maxmagnitude" in features:
|
||||
acc_features["acc_" + day_segment + "_maxmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].max()
|
||||
if "minmagnitude" in features:
|
||||
acc_features["acc_" + day_segment + "_minmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].min()
|
||||
if "avgmagnitude" in features:
|
||||
acc_features["acc_" + day_segment + "_avgmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].mean()
|
||||
if "medianmagnitude" in features:
|
||||
acc_features["acc_" + day_segment + "_medianmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].median()
|
||||
if "stdmagnitude" in features:
|
||||
acc_features["acc_" + day_segment + "_stdmagnitude"] = acc_data.groupby(["local_date"])["magnitude"].std()
|
||||
|
||||
# get extertional activity features
|
||||
# reference: https://jamanetwork.com/journals/jamasurgery/fullarticle/2753807
|
||||
|
||||
# drop rows where we only have one row per minute (no variance)
|
||||
acc_data = dropRowsWithCertainThreshold(acc_data, 1)
|
||||
if not acc_data.empty:
|
||||
# check if the participant performs exertional activity for each minute
|
||||
acc_minute = pd.DataFrame()
|
||||
acc_minute["isexertionalactivity"] = (acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_0"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_1"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_2"].var()).apply(lambda x: 1 if x > 0.15 * (9.807 ** 2) else 0)
|
||||
acc_minute.reset_index(inplace=True)
|
||||
|
||||
if "ratioexertionalactivityepisodes" in features:
|
||||
acc_features["acc_" + day_segment + "_ratioexertionalactivityepisodes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].sum()/acc_minute.groupby(["local_date"])["isexertionalactivity"].count()
|
||||
if "sumexertionalactivityepisodes" in features:
|
||||
acc_features["acc_" + day_segment + "_sumexertionalactivityepisodes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].sum()
|
||||
|
||||
longest_exertionalactivity_episodes, count_exertionalactivity_episodes = getActivityEpisodes(acc_minute, 1)
|
||||
longest_nonexertionalactivity_episodes, count_nonexertionalactivity_episodes = getActivityEpisodes(acc_minute, 0)
|
||||
if "longestexertionalactivityepisode" in features:
|
||||
acc_features["acc_" + day_segment + "_longestexertionalactivityepisode"] = longest_exertionalactivity_episodes["exertional_episodes"]
|
||||
if "longestnonexertionalactivityepisode" in features:
|
||||
acc_features["acc_" + day_segment + "_longestnonexertionalactivityepisode"] = longest_nonexertionalactivity_episodes["nonexertional_episodes"]
|
||||
if "countexertionalactivityepisodes" in features:
|
||||
acc_features["acc_" + day_segment + "_countexertionalactivityepisodes"] = count_exertionalactivity_episodes["exertional_episodes"]
|
||||
if "countnonexertionalactivityepisodes" in features:
|
||||
acc_features["acc_" + day_segment + "_countnonexertionalactivityepisodes"] = count_nonexertionalactivity_episodes["nonexertional_episodes"]
|
||||
|
||||
acc_features = acc_features.reset_index()
|
||||
|
||||
acc_features.to_csv(snakemake.output[0], index=False)
|
||||
Loading…
Reference in New Issue