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.. _rapids_features:
RAPIDS Features
===============
Global Parameters
"""""""""""""""""
.. _sensor-list:
- ``SENSORS`` - List of sensors to include in the pipeline that have to match existent tables in your AWARE_ database. See SENSORS_ variable in ``config`` file.
.. _fitbit-table:
- ``FITBIT_TABLE`` - The name of table in your database that contains Fitbit data. Its ``fitbit_data`` field should contain the data coming from the Fitbit API in JSON format.
.. _fitbit-sensors:
- ``FITBIT_SENSORS`` - The list of sensors to be parsed from the fitbit table: ``heartrate``, ``steps``, ``sleep``.
.. _pid:
- ``PID`` - The list of participant ids to be included in the analysis. These should match the names of the files created in the ``data/external`` directory (:ref:`see more details<db-configuration>`).
.. _day-segments:
- ``DAY_SEGMENTS`` - The list of day epochs that features can be segmented into: ``daily``, ``morning`` (6am-12pm), ``afternnon`` (12pm-6pm), ``evening`` (6pm-12am) and ``night`` (12am-6am). This list can be modified globally or on a per sensor basis. See DAY_SEGMENTS_ in ``config`` file.
.. _timezone:
- ``TIMEZONE`` - The time zone where data was collected. Use the timezone names from this `List of Timezones`_. Double check your chosen name is correct, for example US Eastern Time is named New America/New_York, not EST.
.. _database_group:
- ``DATABASE_GROUP`` - The name of your database credentials group, it should match the one in ``.env`` (:ref:`see the datbase configuration<db-configuration>`).
.. _download-dataset:
- ``DOWNLOAD_DATASET``
- ``GROUP``. Credentials group to connect to the database containing ``SENSORS``. By default it points to ``DATABASE_GROUP``.
.. _readable-datetime:
- ``READABLE_DATETIME`` - Configuration to convert UNIX timestamps into readbale date time strings.
- ``FIXED_TIMEZONE``. See ``TIMEZONE`` above. This assumes that all data of all participants was collected within one time zone.
- Support for multiple time zones for each participant coming soon.
.. _phone-valid-sensed-days:
- ``PHONE_VALID_SENSED_DAYS``.
Contains three attributes: ``BIN_SIZE``, ``MIN_VALID_HOURS``, ``MIN_BINS_PER_HOUR``.
On any given day, Aware could have sensed data only for a few minutes or for 24 hours. Daily estimates of features should be considered more reliable the more hours Aware was running and logging data (for example, 10 calls logged on a day when only one hour of data was recorded is a less reliable measurement compared to 10 calls on a day when 23 hours of data were recorded.
Therefore, we define a valid hour as those that contain at least a certain number of valid bins. In turn, a valid bin are those that contain at least one row of data from any sensor logged within that period. We divide an hour into N bins of size ``BIN_SIZE`` (in minutes) and we mark an hour as valid if contains at least ``MIN_BINS_PER_HOUR`` of valid bins (out of the total possible number of bins that can be captured in an hour i.e. out of 60min/``BIN_SIZE`` bins). Days with valid sensed hours less than ``MIN_VALID_HOURS`` will be excluded form the output of this file. See PHONE_VALID_SENSED_DAYS_ in ``config.yaml``.
In RAPIDS, you will find that we use ``phone_sensed_bins`` (a list of all valid and invalid bins of all monitored days) to improve the estimation of features that are ratios over time periods like ``episodepersensedminutes`` of :ref:`Screen<screen-sensor-doc>`.
.. _individual-sensor-settings:
.. _sms-sensor-doc:
SMS
"""""
See `SMS Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
**Snakefile Entry:**
.. - Download raw SMS dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable datetime to SMS dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Extract SMS features:
| ``expand("data/processed/{pid}/sms_{sms_type}_{day_segment}.csv".``
| ``pid=config["PIDS"],``
| ``sms_type = config["SMS"]["TYPES"],``
| ``day_segment = config["SMS"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/sms_features`` - See the sms_features_ rule.
- **Script:** ``src/features/sms_features.R`` - See the sms_features.R_ script.
.. _sms-parameters:
**SMS Rule Parameters:**
============ ===================
Name Description
============ ===================
sms_type The particular ``sms_type`` that will be analyzed. The options for this parameter are ``received`` or ``sent``.
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the dataset. These features are available for both ``sent`` and ``received`` SMS messages. See :ref:`Available SMS Features <sms-available-features>` Table below
============ ===================
.. _sms-available-features:
**Available SMS Featues**
The following table shows a list of the available featues for both ``sent`` and ``received`` SMS.
========================= ========= =============
Name Units Description
========================= ========= =============
count SMS Number of SMS of type ``sms_type`` that occurred during a particular ``day_segment``.
distinctcontacts contacts Number of distinct contacts that are associated with a particular ``sms_type`` during a particular ``day_segment``.
timefirstsms minutes Number of minutes between 12:00am (midnight) and the first ``SMS`` of a particular ``sms_type``.
timelastsms minutes Number of minutes between 12:00am (midnight) and the last ``SMS`` of a particular ``sms_type``.
countmostfrequentcontact SMS The count of the number of ``SMS`` messages of a particular ``sms_type`` for the most contacted contact for a particular ``day_segment``.
========================= ========= =============
**Assumptions/Observations:**
#. ``TYPES`` and ``FEATURES`` keys need to match. From example::
SMS:
TYPES: [sent]
FEATURES:
sent: [count, distinctcontacts, timefirstsms, timelastsms, countmostfrequentcontact]
In the above config setting code the ``TYPE`` ``sent`` matches the ``FEATURES`` key ``sent``.
.. _call-sensor-doc:
Calls
""""""
See `Call Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
- iOS
**Snakefile Entry:**
.. - Download raw Calls dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable datetime to Calls dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Extract Calls Features
| ``expand("data/processed/{pid}/call_{call_type}_{segment}.csv",``
| ``pid=config["PIDS"],``
| ``call_type=config["CALLS"]["TYPES"],``
| ``segment = config["CALLS"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/call_features`` - See the call_features_ rule.
- **Script:** ``src/features/call_features.R`` - See the call_features.R_ script.
.. _calls-parameters:
**Call Rule Parameters:**
============ ===================
Name Description
============ ===================
call_type The particular ``call_type`` that will be analyzed. The options for this parameter are ``incoming``, ``outgoing`` or ``missed``.
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the calls dataset. Note that the same features are available for both ``incoming`` and ``outgoing`` calls, while ``missed`` calls has its own set of features. See :ref:`Available Incoming and Outgoing Call Features <available-in-and-out-call-features>` Table and :ref:`Available Missed Call Features <available-missed-call-features>` Table below.
============ ===================
.. _available-in-and-out-call-features:
**Available Incoming and Outgoing Call Features**
The following table shows a list of the available features for ``incoming`` and ``outgoing`` calls.
========================= ========= =============
Name Units Description
========================= ========= =============
count calls Number of calls of a particular ``call_type`` occurred during a particular ``day_segment``.
distinctcontacts contacts Number of distinct contacts that are associated with a particular ``call_type`` for a particular ``day_segment``
meanduration minutes The mean duration of all calls of a particular ``call_type`` during a particular ``day_segment``.
sumduration minutes The sum of the duration of all calls of a particular ``call_type`` during a particular ``day_segment``.
minduration minutes The duration of the shortest call of a particular ``call_type`` during a particular ``day_segment``.
maxduration minutes The duration of the longest call of a particular ``call_type`` during a particular ``day_segment``.
stdduration minutes The standard deviation of the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``.
modeduration minutes The mode of the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``.
entropyduration nats The estimate of the Shannon entropy for the the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``.
timefirstcall minutes The time in minutes between 12:00am (midnight) and the first call of ``call_type``.
timelastcall minutes The time in minutes between 12:00am (midnight) and the last call of ``call_type``.
countmostfrequentcontact calls The number of calls of a particular ``call_type`` during a particular ``day_segment`` of the most frequent contact throughout the monitored period.
========================= ========= =============
.. _available-missed-call-features:
**Available Missed Call Features**
The following table shows a list of the available features for ``missed`` calls.
========================= ========= =============
Name Units Description
========================= ========= =============
count calls Number of ``missed`` calls that occurred during a particular ``day_segment``.
distinctcontacts contacts Number of distinct contacts that are associated with ``missed`` calls for a particular ``day_segment``
timefirstcall minutes The time in minutes from 12:00am (Midnight) that the first ``missed`` call occurred.
timelastcall minutes The time in minutes from 12:00am (Midnight) that the last ``missed`` call occurred.
countmostfrequentcontact calls The number of ``missed`` calls during a particular ``day_segment`` of the most frequent contact throughout the monitored period.
========================= ========= =============
**Assumptions/Observations:**
#. ``TYPES`` and ``FEATURES`` keys need to match. From example::
CALLS:
TYPES: [missed]
FEATURES:
missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]
In the above config setting code the ``TYPE`` ``missed`` matches the ``FEATURES`` key ``missed``.
.. _bluetooth-sensor-doc:
Bluetooth
""""""""""
See `Bluetooth Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
- iOS
**Snakefile Entry:**
.. - Download raw Bluetooth dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable datetime to Bluetooth dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Extract Bluetooth Features
| ``expand("data/processed/{pid}/bluetooth_{segment}.csv",``
| ``pid=config["PIDS"],``
| ``segment = config["BLUETOOTH"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/bluetooth_features`` - See the bluetooth_feature_ rule.
- **Script:** ``src/features/bluetooth_features.R`` - See the bluetooth_features.R_ script.
.. _bluetooth-parameters:
**Bluetooth Rule Parameters:**
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the Bluetooth dataset. See :ref:`Available Bluetooth Features <bluetooth-available-features>` Table below
============ ===================
.. _bluetooth-available-features:
**Available Bluetooth Features**
The following table shows a list of the available features for Bluetooth.
=========================== ========= =============
Name Units Description
=========================== ========= =============
countscans devices Number of scanned devices during a ``day_segment``, a device can be detected multiple times over time and these appearances are counted separately
uniquedevices devices Number of unique devices during a ``day_segment`` as identified by their hardware address
countscansmostuniquedevice scans Number of scans of the most scanned device during a ``day_segment`` across the whole monitoring period
=========================== ========= =============
**Assumptions/Observations:** N/A
.. _accelerometer-sensor-doc:
Accelerometer
""""""""""""""
See `Accelerometer Config Code`_
**Available epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available platforms:**
- Android
- iOS
**Snakefile entry:**
.. - Download raw Accelerometer dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable datetime to Accelerometer dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Extract Accelerometer Features
| ``expand("data/processed/{pid}/accelerometer_{day_segment}.csv",``
| ``pid=config["PIDS"],``
| ``day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"]),``
**Rule chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/accelerometer_features`` - See the accelerometer_features_ rule.
- **Script:** ``src/features/accelerometer_features.py`` - See the accelerometer_features.py_ script.
.. _Accelerometer-parameters:
**Accelerometer Rule Parameters:**
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the dataset. See :ref:`Available Accelerometer Features <accelerometer-available-features>` Table below
============ ===================
.. _accelerometer-available-features:
**Available Accelerometer Features**
The following table shows a list of the available features the accelerometer sensor data for a particular ``day_segment``.
==================================== ============== =============
Name Units Description
==================================== ============== =============
maxmagnitude m/s\ :sup:`2` The maximum magnitude of acceleration (:math:`\|acceleration\| = \sqrt{x^2 + y^2 + z^2}`).
minmagnitude m/s\ :sup:`2` The minimum magnitude of acceleration.
avgmagnitude m/s\ :sup:`2` The average magnitude of acceleration.
medianmagnitude m/s\ :sup:`2` The median magnitude of acceleration.
stdmagnitude m/s\ :sup:`2` The standard deviation of acceleration.
ratioexertionalactivityepisodes The ratio of exertional activity time periods to total time periods.
sumexertionalactivityepisodes minutes Total duration of all exertional activity episodes during ``day_segment``.
longestexertionalactivityepisode minutes Duration of the longest exertional activity episode during ``day_segment``.
longestnonexertionalactivityepisode minutes Duration of the longest non-exertional activity episode during ``day_segment``.
countexertionalactivityepisodes episodes Number of the exertional activity episodes during ``day_segment``.
countnonexertionalactivityepisodes episodes Number of the non-exertional activity episodes during ``day_segment``.
==================================== ============== =============
**Assumptions/Observations:** N/A
.. _applications-foreground-sensor-doc:
Applications Foreground
""""""""""""""""""""""""
See `Applications Foreground Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
- iOS
**Snakefile entry:**
.. - Download raw Applications Foreground dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable dateime Applications Foreground dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Genre categorization of Applications Foreground dataset: ``expand("data/interim/{pid}/applications_foreground_with_datetime_with_genre.csv", pid=config["PIDS"]),``
- Extract Applications Foreground Features:
| ``expand("data/processed/{pid}/applications_foreground_{day_segment}.csv",``
| ``pid=config["PIDS"],``
| ``day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/application_genres`` - See the application_genres_ rule
- **Script:** ``../src/data/application_genres.R`` - See the application_genres.R_ script
- **Rule:** ``rules/features.snakefile/applications_foreground_features`` - See the applications_foreground_features_ rule.
- **Script:** ``src/features/applications_foreground_features.py`` - See the applications_foreground_features.py_ script.
.. _applications-foreground-parameters:
**Applications Foreground Rule Parameters:**
==================== ===================
Name Description
==================== ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
single_categories A single category of apps that will be included for the data collection. The available categories can be defined in the ``APPLICATION_GENRES`` in the ``config`` file. See :ref:`Assumtions and Observations <applications-foreground-observations>`.
multiple_categories Categories of apps that will be included for the data collection. The available categories can be defined in the ``APPLICATION_GENRES`` in the ``config`` file. See :ref:`Assumtions and Observations <applications-foreground-observations>`.
single_apps Any Android app can be included in the list of apps used to collect data by adding the package name to this list. (E.g. Youtube)
excluded_categories Categories of apps that will be excluded for the data collection. The available categories can be defined in the ``APPLICATION_GENRES`` in the ``config`` file. See :ref:`Assumtions and Observations <applications-foreground-observations>`.
excluded_apps Any Android app can be excluded from the list of apps used to collect data by adding the package name to this list.
features The different measures that can be retrieved from the dataset. See :ref:`Available Applications Foreground Features <applications-foreground-available-features>` Table below
==================== ===================
.. _applications-foreground-available-features:
**Available Applications Foreground Features**
The following table shows a list of the available features for the Applications Foreground dataset
================== ========= =============
Name Units Description
================== ========= =============
count apps Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app).
timeoffirstuse contacts The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a ``day_segment``.
timeoflastuse minutes The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a ``day_segment``.
frequencyentropy nats The entropy of the used apps within a category during a ``day_segment`` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app.
================== ========= =============
.. _applications-foreground-observations:
**Assumptions/Observations:**
The ``APPLICATION_GENRES`` configuration (See `Application Genres Config`_ setting defines that catalogue of categories of apps that available for the pipeline. The ``CATALOGUE_SOURCE`` defines the source of the catalogue which can be ``FILE`` i.e. a custom file like the file provided with this project (See `Custom Catalogue File`_) or ``GOOGLE`` which is category classifications provided by Google. The ``CATALOGUE_FILE`` variable defines the path to the location of the custom file that contains the custom app catalogue. If ``CATALOGUE_SOURCE`` is equal to ``FILE``, the ``UPDATE_CATALOGUE_FILE`` variable specifies (``TRUE`` or ``FALSE``) whether or not to update ``CATALOGUE_FILE``, if ``CATALOGUE_SOURCE`` is equal to ``GOOGLE`` all scraped genres will be saved to ``CATALOGUE_FILE``. The ``SCRAPE_MISSING_GENRES`` is a ``TRUE`` or ``FALSE`` variable that specifies whether or not to scrape missing genres, only effective if ``CATALOGUE_SOURCE`` is equal to ``FILE``. If ``CATALOGUE_SOURCE`` is equal to ``GOOGLE``, all genres are scraped anyway. It should be noted that the ``top1global`` option finds and uses the most used app for that participant for the study.
.. _battery-sensor-doc:
Battery
"""""""""
See `Battery Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
- iOS
**Snakefile entry:**
.. - Download raw Battery dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable dateime to Battery dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Extract the deltas in Battery charge : ``expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"]),``
- Extract Battery Features:
| ``expand("data/processed/{pid}/battery_{day_segment}.csv",``
| ``pid=config["PIDS"],``
| ``day_segment = config["BATTERY"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/battery_deltas`` - See the battery_deltas_ rule.
- **Script:** ``src/features/battery_deltas.R`` - See the battery_deltas.R_ script.
- **Rule:** ``rules/features.snakefile/battery_features`` - See the battery_features_ rule
- **Script:** ``src/features/battery_features.py`` - See the battery_features.py_ script.
.. _battery-parameters:
**Battery Rule Parameters:**
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the Battery dataset. See :ref:`Available Battery Features <battery-available-features>` Table below
============ ===================
.. _battery-available-features:
**Available Battery Features**
The following table shows a list of the available features for Battery data.
===================== =============== =============
Name Units Description
===================== =============== =============
countdischarge episodes Number of discharging episodes.
sumdurationdischarge hours The total duration of all discharging episodes.
countcharge episodes Number of battery charging episodes.
sumdurationcharge hours The total duration of all charging episodes.
avgconsumptionrate episodes/hours The average of all episodes’ consumption rates. An episode’s consumption rate is defined as the ratio between its battery delta and duration
maxconsumptionrate episodes/hours The highest of all episodes’ consumption rates. An episode’s consumption rate is defined as the ratio between its battery delta and duration
===================== =============== =============
**Assumptions/Observations:**
.. _google-activity-recognition-sensor-doc:
Google Activity Recognition
""""""""""""""""""""""""""""
See `Google Activity Recognition Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
**Snakefile entry:**
.. - Download raw Google Activity Recognition dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable dateime to Google Activity Recognition dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Extract the deltas in Google Activity Recognition dataset: ``expand("data/processed/{pid}/plugin_google_activity_recognition_deltas.csv", pid=config["PIDS"]),``
- Extract Sensor Features:
| ``expand("data/processed/{pid}/google_activity_recognition_{segment}.csv",pid=config["PIDS"],``
| ``segment = config["GOOGLE_ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/google_activity_recognition_deltas`` - See the google_activity_recognition_deltas_ rule.
- **Script:** ``src/features/google_activity_recognition_deltas.R`` - See the google_activity_recognition_deltas.R_ script.
- **Rule:** ``rules/features.snakefile/activity_features`` - See the activity_features_ rule.
- **Script:** ``ssrc/features/google_activity_recognition.py`` - See the google_activity_recognition.py_ script.
.. _google-activity-recognition-parameters:
**Google Activity Recognition Rule Parameters:**
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the Google Activity Recognition dataset. See :ref:`Available Google Activity Recognition Features <google-activity-recognition-available-features>` Table below
============ ===================
.. _google-activity-recognition-available-features:
**Available Google Activity Recognition Features**
The following table shows a list of the available features for the Google Activity Recognition dataset.
====================== ============ =============
Name Units Description
====================== ============ =============
count rows Number of detect activity events (rows).
mostcommonactivity factor The most common activity.
countuniqueactivities activities Number of unique activities.
activitychangecount transitions Number of transitions between two different activities; still to running for example.
sumstationary minutes The total duration of episodes of still and tilting (phone) activities.
summobile minutes The total duration of episodes of on foot, running, and on bicycle activities
sumvehicle minutes The total duration of episodes of on vehicle activity
====================== ============ =============
**Assumptions/Observations:** N/A
.. _light-doc:
Light
"""""""
See `Light Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
**Snakefile entry:**
.. - Download raw Sensor dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable dateime to Sensor dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Extract Light Features:
| ``expand("data/processed/{pid}/light_{day_segment}.csv",``
| ``pid=config["PIDS"],``
| ``day_segment = config["LIGHT"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/light_features`` - See the light_features_ rule.
- **Script:** ``src/features/light_features.py`` - See the light_features.py_ script.
.. _light-parameters:
**Light Rule Parameters:**
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the Light dataset. See :ref:`Available Light Features <light-available-features>` Table below
============ ===================
.. _light-available-features:
**Available Light Features**
The following table shows a list of the available features for the Light dataset.
=========== ========= =============
Name Units Description
=========== ========= =============
count rows Number light sensor rows recorded.
maxlux lux The maximum ambient luminance.
minlux lux The minimum ambient luminance.
avglux lux The average ambient luminance.
medianlux lux The median ambient luminance.
stdlux lux The standard deviation of ambient luminance.
=========== ========= =============
**Assumptions/Observations:** N/A
.. _location-sensor-doc:
Location (Barnett’s) Features
""""""""""""""""""""""""""""""
Barnett’s location features are based on the concept of flights and pauses. GPS coordinates are converted into a
sequence of flights (straight line movements) and pauses (time spent stationary). Data is imputed before features
are computed (https://arxiv.org/abs/1606.06328)
See `Location (Barnett’s) Config Code`_
**Available Epochs:**
- daily
**Available Platforms:**
- Android
- iOS
**Snakefile entry:**
.. - Download raw Sensor dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
.. - Apply readable dateime to Sensor dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Extract Sensor Features: ``expand("data/processed/{pid}/location_barnett.csv", pid=config["PIDS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/phone_sensed_bins`` - See the phone_sensed_bins_ rule.
- **Script:** ``src/data/phone_sensed_bins.R`` - See the phone_sensed_bins.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/resample_fused_location`` - See the resample_fused_location_ rule.
- **Script:** ``src/data/resample_fused_location.R`` - See the resample_fused_location.R_ script.
- **Rule:** ``rules/features.snakefile/location_barnett_features`` - See the location_barnett_features_ rule.
- **Script:** ``src/features/location_barnett_features.R`` - See the location_barnett_features.R_ script.
.. _location-parameters:
**Location Rule Parameters:**
================= ===================
Name Description
================= ===================
location_to_use The specifies which of the location data will be use in the analysis. Possible options are ``ALL``, ``ALL_EXCEPT_FUSED`` OR ``RESAMPLE_FUSED``
accuracy_limit This is in meters. The sensor drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius specified.
timezone The timezone used to calculate location.
features The different measures that can be retrieved from the Location dataset. See :ref:`Available Location Features <location-available-features>` Table below
================= ===================
.. _location-available-features:
**Available Location Features**
The following table shows a list of the available features for Location dataset.
================ ========= =============
Name Units Description
================ ========= =============
hometime minutes Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius.
disttravelled meters Total distance travelled over a day (flights).
rog meters The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place.
maxdiam meters The maximum diameter is the largest distance between any two pauses.
maxhomedist meters The maximum distance from home in meters.
siglocsvisited locations The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another.
avgflightlen meters Mean length of all flights.
stdflightlen meters Standard deviation of the length of all flights.
avgflightdur meters Mean duration of all flights.
stdflightdur meters The standard deviation of the duration of all flights.
probpause The fraction of a day spent in a pause (as opposed to a flight)
siglocentropy nats Shannon’s entropy measurement based on the proportion of time spent at each significant location visited during a day.
circdnrtn A continuous metric quantifying a person’s circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day.
wkenddayrtn Weekend Same as circdnrtn but computed separately for weekends and weekdays.
================ ========= =============
**Assumptions/Observations:**
*Significant Locations Identified*
(i.e. The clustering method used)
Significant locations are determined using K-means clustering on locations that a patient visit over the course of the period of data collection. By setting K=K+1 and repeat clustering until two significant locations are within 100 meters of one another, the results from the previous step (K-1) can be used as the total number of significant locations. See `Beiwe Summary Statistics`_.
*Definition of Stationarity*
(i.e., The length of time a person have to be not moving to qualify)
This is based on a Pause-Flight model, The parameters used is a minimum pause duration of 300sec and a minimum pause distance of 60m. See the `Pause-Flight Model`_.
*The Circadian Calculation*
For a detailed description of how this measure is calculated, see Canzian and Musolesi's 2015 paper in the Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, titled "Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis." Their procedure was followed using 30-min increments as a bin size. See `Beiwe Summary Statistics`_.
.. _screen-sensor-doc:
Screen
""""""""
See `Screen Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Android
- iOS
**Snakefile entry:**
.. - Download raw Screen dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Apply readable dateime to Screen dataset: ``expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SENSORS"]),``
- Extract the deltas from the Screen dataset: expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"]),
- Extract Screen Features:
| ``expand("data/processed/{pid}/screen_{day_segment}.csv",``
| ``pid=config["PIDS"],``
| ``day_segment = config["SCREEN"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/readable_datetime`` - See the readable_datetime_ rule.
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/screen_deltas`` - See the screen_deltas_ rule.
- **Script:** ``src/features/screen_deltas.R`` - See the screen_deltas.R_ script.
- **Rule:** ``rules/features.snakefile/screen_features`` - See the screen_features_ rule.
- **Script:** ``src/features/screen_features.py`` - See the screen_features.py_ script.
.. _screen-parameters:
**Screen Rule Parameters:**
========================= ===================
Name Description
========================= ===================
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
reference_hour_first_use The reference point from which ``firstuseafter`` is to be computed, default is midnight
features_deltas The different measures that can be retrieved from the episodes extracted from the Screen dataset. See :ref:`Available Screen Episodes Features <screen-episodes-available-features>` Table below
episode_types The action that defines an episode
========================= ===================
.. _screen-events-available-features:
..
**Available Screen Events Features**
The following table shows a list of the available features for Screen Events.
================= ============== =============
Name Units Description
================= ============== =============
counton `ON` events Count on: A count of screen `ON` events (only available for Android)
countunlock Unlock events Count unlock: A count of screen unlock events.
unlocksperminute Unlock events Unlock events per minute: The average of the number of unlock events that occur in a minute
================= ============== =============
.. _screen-episodes-available-features:
**Available Screen Episodes Features**
The following table shows a list of the available features for Screen Episodes.
========================= ================= =============
Name Units Description
========================= ================= =============
sumduration seconds Total duration of all unlock episodes.
maxduration seconds Longest duration of any unlock episode.
minduration seconds Shortest duration of any unlock episode.
avgduration seconds Average duration of all unlock episodes.
stdduration seconds Standard deviation duration of all unlock episodes.
countepisode episodes Number of all unlock episodes
episodepersensedminutes episodes/minute The ratio between the total number of episodes in an epoch divided by the total time (minutes) the phone was sensing data.
firstuseafter seconds Seconds until the first unlock episode.
========================= ================= =============
**Assumptions/Observations:**
An ``unlock`` episode is considered as the time between an ``unlock`` event and a ``lock`` event. iOS recorded these episodes reliable (albeit some duplicated ``lock`` events within milliseconds from each other). However, in Android there are some events unrelated to the screen state because of multiple consecutive ``unlock``/``lock`` events, so we keep the closest pair. In the experiments these are less than 10% of the screen events collected. This happens because ``ACTION_SCREEN_OFF`` and ``ON`` are "sent when the device becomes non-interactive which may have nothing to do with the screen turning off". Additionally in Android it is possible to measure the time spent on the ``lock`` screen onto the ``unlock`` event and the total screen time (i.e. ``ON`` to ``OFF``) events but we are only keeping ``unlock`` episodes (``unlock`` to ``OFF``) to be consistent with iOS.
.. _fitbit-heart-rate-sensor-doc:
Fitbit: Heart Rate
"""""""""""""""""""
See `Fitbit: Heart Rate Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Fitbit
**Snakefile entry:**
.. - Download raw Fitbit: Heart Rate dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["FITBIT_TABLE"]),``
.. - Apply readable datetime to Fitbit: Heart Rate dataset:
..
| ``expand("data/raw/{pid}/fitbit_{fitbit_sensor}_with_datetime.csv",``
| ``pid=config["PIDS"],``
| ``fitbit_sensor=config["FITBIT_SENSORS"]),``
- Extract Sensor Features:
| ``expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv",``
| ``pid=config["PIDS"],``
| ``day_segment = config["HEARTRATE"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/fitbit_with_datetime`` - See the fitbit_with_datetime_ rule.
- **Script:** ``src/data/fitbit_readable_datetime.py`` - See the fitbit_readable_datetime.py_ script.
- **Rule:** ``rules/features.snakefile/fitbit_heartrate_features`` - See the fitbit_heartrate_features_ rule.
- **Script:** ``src/features/fitbit_heartrate_features.py`` - See the fitbit_heartrate_features.py_ script.
.. _fitbit-heart-rate-parameters:
**Fitbit: Heart Rate Rule Parameters:**
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the Fitbit: Heart Rate dataset.
See :ref:`Available Fitbit: Heart Rate Features <fitbit-heart-rate-available-features>` Table below
============ ===================
.. _fitbit-heart-rate-available-features:
**Available Fitbit: Heart Rate Features**
The following table shows a list of the available features for the Fitbit: Heart Rate dataset.
================== =========== =============
Name Units Description
================== =========== =============
maxhr beats/mins The maximum heart rate.
minhr beats/mins The minimum heart rate.
avghr beats/mins The average heart rate.
medianhr beats/mins The median heart rate.
modehr beats/mins The mode heart rate.
stdhr beats/mins The standard deviation of heart rate.
diffmaxmodehr beats/mins Diff max mode heart rate: The maximum heart rate minus mode heart rate.
diffminmodehr beats/mins Diff min mode heart rate: The mode heart rate minus minimum heart rate.
entropyhr Entropy heart rate: The entropy of heart rate.
lengthoutofrange minutes Length out of range: The duration of time the heart rate is in the ``out_of_range`` zone in minute.
lengthfatburn minutes Length fat burn: The duration of time the heart rate is in the ``fat_burn`` zone in minute.
lengthcardio minutes Length cardio: The duration of time the heart rate is in the ``cardio`` zone in minute.
lengthpeak minutes Length peak: The duration of time the heart rate is in the ``peak`` zone in minute
================== =========== =============
**Assumptions/Observations:** Heart rate zones contain 4 zones: ``out_of_range`` zone, ``fat_burn`` zone, ``cardio`` zone, and ``peak`` zone. Please refer to the `Fitbit documentation`_ for detailed information of how to define those zones.
.. _fitbit-steps-sensor-doc:
Fitbit: Steps
"""""""""""""""
See `Fitbit: Steps Config Code`_
**Available Epochs:**
- daily
- morning
- afternoon
- evening
- night
**Available Platforms:**
- Fitbit
**Snakefile entry:**
.. - Download raw Fitbit: Steps dataset: ``expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["FITBIT_TABLE"]),``
..
- Apply readable datetime to Fitbit: Steps dataset:
| ``expand("data/raw/{pid}/fitbit_{fitbit_sensor}_with_datetime.csv",``
| ``pid=config["PIDS"],``
| ``fitbit_sensor=config["FITBIT_SENSORS"]),``
- Extract Fitbit: Steps Features:
| ``expand("data/processed/{pid}/fitbit_step_{day_segment}.csv",``
| ``pid=config["PIDS"],``
| ``day_segment = config["STEP"]["DAY_SEGMENTS"]),``
**Rule Chain:**
- **Rule:** ``rules/preprocessing.snakefile/download_dataset`` - See the download_dataset_ rule.
- **Script:** ``src/data/download_dataset.R`` - See the download_dataset.R_ script.
- **Rule:** ``rules/preprocessing.snakefile/fitbit_with_datetime`` - See the fitbit_with_datetime_ rule.
- **Script:** ``src/data/fitbit_readable_datetime.py`` - See the fitbit_readable_datetime.py_ script.
- **Rule:** ``rules/features.snakefile/fitbit_step_features`` - See the fitbit_step_features.py_ rule.
- **Script:** ``src/features/fitbit_step_features.py`` - See the fitbit_step_features.py_ script.
.. _fitbit-steps-parameters:
**Fitbit: Steps Rule Parameters:**
======================= ===================
Name Description
======================= ===================
day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
features The different measures that can be retrieved from the dataset. See :ref:`Available Fitbit: Steps Features <fitbit-steps-available-features>` Table below
threshold_active_bout The maximum number of steps per minute necessary for a bout to be ``sedentary``. That is, if the step count per minute is greater than this value the bout has a status of ``active``.
======================= ===================
.. _fitbit-steps-available-features:
**Available Fitbit: Steps Features**
The following table shows a list of the available features for the Fitbit: Steps dataset.
========================= ========= =============
Name Units Description
========================= ========= =============
sumallsteps steps Sum all steps: The total step count.
maxallsteps steps Max all steps: The maximum step count
minallsteps steps Min all steps: The minimum step count
avgallsteps steps Avg all steps: The average step count
stdallsteps steps Std all steps: The standard deviation of step count
countsedentarybout bouts Count sedentary bout: A count of sedentary bouts
maxdurationsedentarybout minutes Max duration sedentary bout: The maximum duration of sedentary bouts
mindurationsedentarybout minutes Min duration sedentary bout: The minimum duration of sedentary bouts
avgdurationsedentarybout minutes Avg duration sedentary bout: The average duration of sedentary bouts
stddurationsedentarybout minutes Std duration sedentary bout: The standard deviation of the duration of sedentary bouts
countactivebout bouts Count active bout: A count of active bouts
maxdurationactivebout minutes Max duration active bout: The maximum duration of active bouts
mindurationactivebout minutes Min duration active bout: The minimum duration of active bouts
avgdurationactivebout minutes Avg duration active bout: The average duration of active bouts
stddurationactivebout minutes Std duration active bout: The standard deviation of the duration of active bouts
========================= ========= =============
**Assumptions/Observations:** If the step count per minute smaller than the ``THRESHOLD_ACTIVE_BOUT`` (default value is 10), it is defined as sedentary status. Otherwise, it is defined as active status. One active/sedentary bout is a period during with the user is under ``active``/``sedentary`` status.
.. -------------------------Links ------------------------------------ ..
.. _SENSORS: https://github.com/carissalow/rapids/blob/f22d1834ee24ab3bcbf051bc3cc663903d822084/config.yaml#L2
.. _`SMS Config Code`: https://github.com/carissalow/rapids/blob/f22d1834ee24ab3bcbf051bc3cc663903d822084/config.yaml#L38
.. _AWARE: https://awareframework.com/what-is-aware/
.. _`List of Timezones`: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones
.. _sms_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L1
.. _sms_features.R: https://github.com/carissalow/rapids/blob/master/src/features/sms_featues.R
.. _download_dataset: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L9
.. _download_dataset.R: https://github.com/carissalow/rapids/blob/master/src/data/download_dataset.R
.. _readable_datetime: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L21
.. _readable_datetime.R: https://github.com/carissalow/rapids/blob/master/src/data/readable_datetime.R
.. _DAY_SEGMENTS: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L13
.. _PHONE_VALID_SENSED_DAYS: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L60
.. _`Call Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L46
.. _call_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L13
.. _call_features.R: https://github.com/carissalow/rapids/blob/master/src/features/call_features.R
.. _`Bluetooth Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L76
.. _bluetooth_feature: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L63
.. _bluetooth_features.R: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/src/features/bluetooth_features.R
.. _`Accelerometer Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L98
.. _accelerometer_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L124
.. _accelerometer_features.py: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/src/features/accelerometer_featues.py
.. _`Applications Foreground Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L102
.. _`Application Genres Config`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L54
.. _application_genres: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L81
.. _application_genres.R: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/src/data/application_genres.R
.. _applications_foreground_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L135
.. _applications_foreground_features.py: https://github.com/carissalow/rapids/blob/master/src/features/accelerometer_features.py
.. _`Battery Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L84
.. _battery_deltas: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L25
.. _battery_deltas.R: https://github.com/carissalow/rapids/blob/master/src/features/battery_deltas.R
.. _battery_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L86
.. _battery_features.py : https://github.com/carissalow/rapids/blob/master/src/features/battery_features.py
.. _`Google Activity Recognition Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L80
.. _google_activity_recognition_deltas: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L41
.. _google_activity_recognition_deltas.R: https://github.com/carissalow/rapids/blob/master/src/features/google_activity_recognition_deltas.R
.. _activity_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L74
.. _google_activity_recognition.py: https://github.com/carissalow/rapids/blob/master/src/features/google_activity_recognition.py
.. _`Light Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L94
.. _light_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L113
.. _light_features.py: https://github.com/carissalow/rapids/blob/master/src/features/light_features.py
.. _`Location (Barnett’s) Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L70
.. _phone_sensed_bins: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L46
.. _phone_sensed_bins.R: https://github.com/carissalow/rapids/blob/master/src/data/phone_sensed_bins.R
.. _resample_fused_location: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L67
.. _resample_fused_location.R: https://github.com/carissalow/rapids/blob/master/src/data/resample_fused_location.R
.. _location_barnett_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L49
.. _location_barnett_features.R: https://github.com/carissalow/rapids/blob/master/src/features/location_barnett_features.R
.. _`Screen Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L88
.. _screen_deltas: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L33
.. _screen_deltas.R: https://github.com/carissalow/rapids/blob/master/src/features/screen_deltas.R
.. _screen_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L97
.. _screen_features.py: https://github.com/carissalow/rapids/blob/master/src/features/screen_features.py
.. _`Fitbit: Heart Rate Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L113
.. _fitbit_with_datetime: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L94
.. _fitbit_readable_datetime.py: https://github.com/carissalow/rapids/blob/master/src/data/fitbit_readable_datetime.py
.. _fitbit_heartrate_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L151
.. _fitbit_heartrate_features.py: https://github.com/carissalow/rapids/blob/master/src/features/fitbit_heartrate_features.py
.. _`Fitbit: Steps Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L117
.. _fitbit_step_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L162
.. _fitbit_step_features.py: https://github.com/carissalow/rapids/blob/master/src/features/fitbit_step_features.py
.. _`Fitbit documentation`: https://help.fitbit.com/articles/en_US/Help_article/1565
.. _`Custom Catalogue File`: https://github.com/carissalow/rapids/blob/master/data/external/stachl_application_genre_catalogue.csv
.. _top1global: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L108
.. _`Beiwe Summary Statistics`: http://wiki.beiwe.org/wiki/Summary_Statistics
.. _`Pause-Flight Model`: https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxy059/5145908
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