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kaguillera 2020-05-19 12:34:45 -04:00
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docs/features/extracted.rst
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@ -24,7 +24,7 @@ Global Parameters
.. _day-segments:
- ``DAY_SEGMENTS`` - The list of day epochs that feature data 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.
- ``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:
@ -38,7 +38,7 @@ Global Parameters
- ``DOWNLOAD_DATASET``
- ``GROUP`` - Credential group to connect to the database containing ``SENSORS``. By default it points to ``DATABASE_GROUP``.
- ``GROUP``. Credentials group to connect to the database containing ``SENSORS``. By default it points to ``DATABASE_GROUP``.
.. _readable-datetime:
@ -53,7 +53,7 @@ Global Parameters
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.
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``.
@ -105,7 +105,7 @@ See `SMS Config Code`_
- **Script:** ``src/data/readable_datetime.R`` - See the readable_datetime.R_ script.
- **Rule:** ``rules/features.snakefile/sms_features`` - See the sms_feature_ rule.
- **Rule:** ``rules/features.snakefile/sms_features`` - See the sms_features_ rule.
- **Script:** ``src/features/sms_features.R`` - See the sms_features.R_ script.
@ -118,7 +118,7 @@ See `SMS Config Code`_
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_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
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
============ ===================
@ -131,11 +131,11 @@ The following table shows a list of the available featues for both ``sent`` and
========================= ========= =============
Name Units Description
========================= ========= =============
count SMS A count of the number of times that particular ``sms_type`` occurred for a particular ``day_segment``.
distinctcontacts contacts A count of distinct contacts that are associated with a particular ``sms_type`` for a particular ``day_segment``.
timefirstsms minutes The time in minutes from 12:00am (Midnight) that the first of a particular ``sms_type`` occurred.
timelastsms minutes The time in minutes from 12:00am (Midnight) that the last of a particular ``sms_type`` occurred.
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``.
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:**
@ -206,8 +206,8 @@ See `Call Config Code`_
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_segments`` 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.
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:
@ -219,18 +219,18 @@ The following table shows a list of the available features for ``incoming`` and
========================= ========= =============
Name Units Description
========================= ========= =============
count calls A count of the number of times that a particular ``call_type`` occurred for a particular ``day_segment``.
distinctcontacts contacts A count of distinct contacts that are associated with a particular ``call_type`` for a particular ``day_segment``
meanduration minutes The mean duration of all calls for a particular ``call_type`` and ``day_segment``.
sumduration minutes The sum of the duration of all calls for a particular ``call_type`` and ``day_segment``.
minduration minutes The duration of the shortest call for a particular ``call_type`` and ``day_segment``.
maxduration minutes The duration of the longest call for a particular ``call_type`` and ``day_segment``.
stdduration minutes The standard deviation of all the calls for a particular ``call_type`` and ``day_segment``.
modeduration minutes The mode duration of all the calls for a particular ``call_type`` and ``day_segment``.
entropyduration nats The estimate of the Shannon entropy H of the durations of all the calls for a particular ``call_type`` and ``day_segment``.
timefirstcall minutes The time in minutes from 12:00am (Midnight) that the first of ``call_type`` occurred.
timelastcall minutes The time in minutes from 12:00am (Midnight) that the last of ``call_type`` occurred.
countmostfrequentcontact calls The count of the number of calls of a particular ``call_type`` and ``day_segment`` for the most contacted contact.
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:
@ -242,11 +242,11 @@ The following table shows a list of the available features for ``missed`` calls.
========================= ========= =============
Name Units Description
========================= ========= =============
count calls A count of the number of times a ``missed`` call occurred for a particular ``day_segment``.
distinctcontacts contacts A count of distinct contacts whose calls were ``missed``.
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 count of the number of ``missed`` calls for the contact with the most ``missed`` calls.
countmostfrequentcontact calls The number of ``missed`` calls during a particular ``day_segment`` of the most frequent contact throughout the monitored period.
========================= ========= =============
**Assumptions/Observations:**
@ -279,7 +279,7 @@ See `Bluetooth Config Code`_
**Available Platforms:**
- Android
- iOS (Low Energy Devices Only)
- iOS
**Snakefile Entry:**
@ -315,7 +315,7 @@ See `Bluetooth Config Code`_
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
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
============ ===================
@ -328,9 +328,9 @@ The following table shows a list of the available features for Bluetooth.
=========================== ========= =============
Name Units Description
=========================== ========= =============
countscans scans Count of scanned devices during a ``day_segment`` (a scan is a row containing a single Bluetooth device detected by Aware). , a device can be detected multiple times over time and these appearances are counted separately
uniquedevices devices Count of Unique devices during a ``day_segment`` (number of unique devices identified by their hardware address -bt_address field)
countscansmostuniquedevice scans Count of scans of the most scanned device during a ``day_segment`` across the entire study period
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
@ -391,7 +391,7 @@ 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``
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
============ ===================
@ -410,16 +410,15 @@ avgmagnitude m/s\ :sup:`2` The average magnitude o
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 The total duration in minutes of performing exertional activity during the epoch
longestexertionalactivityepisode minutes The duration of the longest episode of performing exertional activity
longestnonexertionalactivityepisode minutes The duration of the longest episode of performing non-exertional activity
countexertionalactivityepisodes episodes The count of the episodes of performing exertional activity
countnonexertionalactivityepisodes episodes The count of the episodes of performing non-exertional activity
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:**
**Assumptions/Observations:** N/A
#. The first six features are computed over the magnitude of the three-axis acceleration vector (x,y,z) the rest are based on exertional and non-exertional activity episodes
.. _applications-foreground-sensor-doc:
@ -440,6 +439,7 @@ See `Applications Foreground Config Code`_
**Available Platforms:**
- Android
- iOS
**Snakefile entry:**
@ -480,7 +480,7 @@ See `Applications Foreground Config Code`_
==================== ===================
Name Description
==================== ===================
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
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)
@ -498,10 +498,10 @@ The following table shows a list of the available features for the Applications
================== ========= =============
Name Units Description
================== ========= =============
count apps A count number of times using ``all_apps``, ``single_app``, ``single_category`` apps or ``multiple_category`` apps. (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 from 12:00am (Midnight) to first use of any app within a category during a ``day_segment``(i.e. ``all_apps``, ``single_app``, ``single_category`` apps or ``multiple_category`` apps).
timeoflastuse minutes The time in minutes from 12:00am (Midnight) to the last of use of any app within a category during a ``day_segment``(i.e. ``all_apps``, ``single_app``, ``single_category`` apps or ``multiple_category`` apps).
frequencyentropy shannons The entropy of the used apps within a category during a ``day_segment`` for ``all_apps``, ``single_category`` apps or ``multiple_category`` apps. (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. There is no entropy for ``single_app`` apos.
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:
@ -571,7 +571,7 @@ See `Battery Config Code`_
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
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
============ ===================
@ -584,10 +584,10 @@ The following table shows a list of the available features for Battery data.
===================== =============== =============
Name Units Description
===================== =============== =============
countdischarge episodes A count of the number of battery discharging episodes
sumdurationdischarge hours The total duration of all discharging episodes (time the phone was discharging)
countcharge episodes A count of the number of battery charging episodes
sumdurationcharge hours The total duration of all charging episodes (time the phone was charging)
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
===================== =============== =============
@ -652,7 +652,7 @@ See `Google Activity Recognition Config Code`_
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
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
============ ===================
@ -665,18 +665,16 @@ The following table shows a list of the available features for the Google Activi
====================== ============ =============
Name Units Description
====================== ============ =============
count rows A count of the number of rows of registered activities.
mostcommonactivity The most common activity.
countuniqueactivities activities A count of the number of unique activities.
activitychangecount transitions A count of any transition between two different activities, sitting to running for example.
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:**
#. These features are based on activity episodes (deltas) which are defined as consecutive detections of the same activity type. The activities should come from `Google’s Activity Recognition API`_: in vehicle, on bicycle, on foot, running, still, tilting, unknown and walking
**Assumptions/Observations:** N/A
.. _light-doc:
@ -730,7 +728,7 @@ See `Light Config Code`_
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
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
============ ===================
@ -743,12 +741,12 @@ The following table shows a list of the available features for the Light dataset
=========== ========= =============
Name Units Description
=========== ========= =============
count rows A count of the number of rows that light sensor recorded.
maxlux lux The maximum ambient luminance in lux units
minlux lux The minimum ambient luminance in lux units
avglux lux The average ambient luminance in lux units
medianlux lux The median ambient luminance in lux units
stdlux lux The standard deviation of ambient luminance in lux units
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
@ -758,7 +756,9 @@ stdlux lux The standard deviation of ambient luminance in lux u
Location (Barnett’s) Features
""""""""""""""""""""""""""""""
This method was originally implemented by Barnett et al.(Barnett & Onnela, 2018), these features are based on the concept of flights and pauses where GPS coordinates are converted into a sequence of straight line movements and stationary clusters, imputing missing mobility traces. This method relies on location coordinates being collected at a regular interval, thus if location data was sensed using AWARE’s Fused location plugin which relies on Google’s Fused location API that only records data when a user’s location has changed, we fill missing intervals with the last known coordinate pair only if the AWARE client was active and therefore the smartphone was collecting data. We re use the code kindly provided by Ian Barnett and reproduce the list of available features, for more details please refer to their paper (Barnett & Onnela, 2018). (https://arxiv.org/abs/1606.06328)
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`_
@ -824,35 +824,33 @@ The following table shows a list of the available features for Location dataset.
================ ========= =============
Name Units Description
================ ========= =============
hometime minutes 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 Distance travelled. This is total distance travelled over a day.
rog meters The Radius of Gyration (RoG). It 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 the centroid is computed. The weights are proportional to the time spent in each place.
maxdiam meters The largest distance in meters between any two pauses.
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 The standard deviation of the length of all flights.
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 Entropy measurement based on the proportion of time spent at each significant location visited during a day.
circdnrtn A continuous feature 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 Circadian routine but computed separately for weekends and weekdays.
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`_.
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 300 seconds and a minimum pause distance of 60m. See the `Pause-Flight Model`_.
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*
@ -940,18 +938,18 @@ episode_types The action that defines an episode
The following table shows a list of the available features for Screen Episodes.
======================== ================= =============
Name Units Description
======================== ================= =============
countepisode episodes A count of the number of all unlock episodes within the ``day_segment``
sumduration seconds The sum of the durations of all unlock episodes
maxduration seconds The maximum duration of any unlock episodes
minduration seconds The minimum duration of any unlock episodes
avgduration seconds The average duration of all unlock episodes
stdduration seconds The standard deviation of the duration of all unlock episodes
episodepersensedminutes episodes/minutes The ratio between the total number of unlock episodes in a ``day_segment`` divided by the total time (minutes) the phone was sensing data
firstuseafter seconds The time in seconds at which the phone was used for the first time in the ``day_segment`` (including daily)
======================== ================= =============
========================= ================= =============
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:**
@ -1015,8 +1013,9 @@ See `Fitbit: Heart Rate Config Code`_
============ ===================
Name Description
============ ===================
day_segment The particular ``day_segments`` 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
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:
@ -1034,18 +1033,16 @@ 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 The maximum heart rate minus mode heart rate.
diffminmodehr beats/mins The mode heart rate minus minimum heart rate.
entropyhr The entropy of heart rate.
lengthoutofrange minutes The duration of time the heart rate is in the ``out_of_range`` zone in minute.
lengthfatburn minutes The duration of time the heart rate is in the ``fat_burn`` zone in minute.
lengthcardio minutes The duration of time the heart rate is in the ``cardio`` zone in minute.
lengthpeak minutes The duration of time the heart rate is in the ``peak`` zone in minute
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.
**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:
@ -1104,10 +1101,9 @@ See `Fitbit: Steps Config Code`_
======================= ===================
Name Description
======================= ===================
day_segment The particular ``day_segments`` 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
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``.
include_zero_step_rows Specifies whether the rows with steps will be used in analysis.
======================= ===================
.. _fitbit-steps-available-features:
@ -1119,27 +1115,24 @@ The following table shows a list of the available features for the Fitbit: Steps
========================= ========= =============
Name Units Description
========================= ========= =============
sumallsteps steps The total step count.
maxallsteps steps The maximum step count
minallsteps steps The minimum step count
avgallsteps steps The average step count
stdallsteps steps The standard deviation of step count
countsedentarybout bouts A count of sedentary bouts
maxdurationsedentarybout minutes The maximum duration of sedentary bouts
mindurationsedentarybout minutes The minimum duration of sedentary bouts
avgdurationsedentarybout minutes The average duration of sedentary bouts
stddurationsedentarybout minutes The standard deviation of the duration of sedentary bouts
sumdurationsedentarybout minutes The sum of durations of sedentary bouts.
countactivebout bouts A count of active bouts
maxdurationactivebout minutes The maximum duration of active bouts
mindurationactivebout minutes The minimum duration of active bouts
avgdurationactivebout minutes The average duration of active bouts
stddurationactivebout minutes The standard deviation of the duration of active bouts
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.
**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 ------------------------------------ ..
@ -1148,7 +1141,7 @@ If the step count per minute smaller than the ``THRESHOLD_ACTIVE_BOUT`` (default
.. _`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_feature: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L1
.. _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
@ -1181,7 +1174,6 @@ If the step count per minute smaller than the ``THRESHOLD_ACTIVE_BOUT`` (default
.. _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
.. _`Google’s Activity Recognition API`: https://developers.google.com/android/reference/com/google/android/gms/location/DetectedActivity
.. _`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