junos
/
rapids
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
rapids/docs/features/phone-data-quality.md

1010 lines
68 KiB
Markdown
Raw Normal View History

Add bluetooth, calls, sms and wifi docs
2020-11-05 01:37:24 +01:00
# Phone Data Quality
## Phone Valid Sensed Bins
A valid bin is any period of `BIN_SIZE` minutes starting from midnight with at least 1 row from any phone sensor. `PHONE_VALID_SENSED_BINS` are used to compute `PHONE_VALID_SENSED_DAYS`, to resample fused location data and to compute some screen features.
!!! hint
`PHONE_VALID_SENSED_DAYS` are an approximation to the time the phone was sensing data so add as many sensors as you have to `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]`
Parameters description for `PHONE_VALID_SENSED_BINS`:
| Key                              | Description|
|-----|------------|
| `[COMPUTE]`| Set to `True` to compute |
| `[BIN_SIZE]` | Size of each bin in minutes |
| `[PHONE_SENSORS]` | One or more sensor config keys (PHONE_MESSAGE) to be used to flag a bing as valid or not (whether or not a bin contains at least one row from any sensor)|
!!! info "Possible values for `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]`"
```yaml
PHONE_MESSAGES
PHONE_CALLS
PHONE_LOCATIONS
PHONE_BLUETOOTH
PHONE_ACTIVITY_RECOGNITION
PHONE_BATTERY
PHONE_SCREEN
PHONE_LIGHT
PHONE_ACCELEROMETER
PHONE_APPLICATIONS_FOREGROUND
PHONE_WIFI_VISIBLE
PHONE_WIFI_CONNECTED
PHONE_CONVERSATION
```
## Phone Valid Sensed Days
On any given day, a phone could have sensed data only for a few minutes or for 24 hours. Features should considered more reliable the more hours the phone was logging data, for example, 10 calls logged on a day when only 1 hour of data was recorded is a less reliable feature compared to 10 calls on a day when 23 hours of data were recorded.
Therefore, we define a valid hour as those that contain a minimum number of valid bins (see above). We mark an hour as valid when contains at least `MIN_VALID_BINS_PER_HOUR` (out of 60min/`BIN_SIZE` bins). In turn, we mark a day as valid if it has at least `MIN_VALID_HOURS_PER_DAY`. You can use `PHONE_VALID_SENSED_DAYS` to manually discard days when not enough data was collected after your features are computed.
Parameters description for `PHONE_VALID_SENSED_DAYS`:
| Key                                                | Description|
|-----|------------|
| `[COMPUTE]`| Set to `True` to compute |
| `[MIN_VALID_BINS_PER_HOUR]` | An array of integer values, 6 by default. Minimum number of valid bins to mark an hour as valid|
| `[MIN_VALID_HOURS_PER_DAY]` | An array of integer values, 16 by default. Minimum number of valid hours to mark a day as valid |
WiFi {#wifi-sensor-doc}
----
See [WiFi Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L172)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:** Android and iOS
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/readable_datetime`
- Rule `rules/features.snakefile/wifi_features`
::: {#wifi-parameters}
**WiFi Rule Parameters (wifi_features):**
:::
Name Description
-------------- -----------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
features Features to be computed, see table below
::: {#wifi-available-features}
**Available WiFi Features**
:::
Name Units Description
---------------------------- --------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------
countscans devices Number of scanned WiFi access points during a `day_segment`, an access point can be detected multiple times over time and these appearances are counted separately
uniquedevices devices Number of unique access point during a `day_segment` as identified by their hardware address
countscansmostuniquedevice scans Number of scans of the most scanned access point during a `day_segment` across the whole monitoring period
**Assumptions/Observations:** Both phone platforms record the wifi
networks a phone is connected to in `sensor_wifi` and those networks
that are being broadcasted around a phone in `wifi`. However, iOS cannot
record any broadcasting network due to API restrictions, therefore iOS
wifi data only exists in `sensor_wifi`.
Accelerometer {#accelerometer-sensor-doc}
-------------
See [Accelerometer Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L118)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:** Android and iOS
**Rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/readable_datetime`
- Rule `rules/features.snakefile/accelerometer_features`
::: {#Accelerometer-parameters}
**Accelerometer Rule Parameters (accelerometer_features):**
:::
Name Description
-------------- -----------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
features Features to be computed, see table below
::: {#accelerometer-available-features}
**Available Accelerometer Features**
:::
Name Units Description
----------------- --------- -------------------------------------------------------------------------------------------
maxmagnitude m/s^2^ The maximum magnitude of acceleration ($\|acceleration\| = \sqrt{x^2 + y^2 + z^2}$).
minmagnitude m/s^2^ The minimum magnitude of acceleration.
avgmagnitude m/s^2^ The average magnitude of acceleration.
medianmagnitude m/s^2^ The median magnitude of acceleration.
stdmagnitude m/s^2^ The standard deviation of acceleration.
sumduration minutes Total duration of all exertional or non-exertional activity episodes.
maxduration minutes Longest duration of any exertional or non-exertional activity episode.
minduration minutes Shortest duration of any exertional or non-exertional activity episode.
avgduration minutes Average duration of any exertional or non-exertional activity episode.
medianduration minutes Median duration of any exertional or non-exertional activity episode.
stdduration minutes Standard deviation of the duration of all exertional or non-exertional activity episodes.
**Assumptions/Observations:**
Exertional activity episodes are based on this paper: Panda N, Solsky I,
Huang EJ, et al. Using Smartphones to Capture Novel Recovery Metrics
After Cancer Surgery. JAMA Surg. 2020;155(2):123--129.
<doi:10.1001/jamasurg.2019.4702>
Applications Foreground {#applications-foreground-sensor-doc}
-----------------------
See [Applications Foreground Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L128)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:** Android
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/readable_datetime`
- Rule `rules/preprocessing.snakefile/application_genres`
- Rule `rules/features.snakefile/applications_foreground_features`
::: {#applications-foreground-parameters}
**Applications Foreground Rule Parameters
(applications_foreground_features):**
:::
Name Description
---------------------- ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
single_categories App categories to be included in the feature extraction computation. See `APPLICATION_GENRES` in this file to add new categories or use the catalogue we provide and read `Assumtions and Observations <applications-foreground-observations>`{.interpreted-text role="ref"} for more information.
multiple_categories You can group multiple categories into meta categories, for example `social: ["socialnetworks", "socialmediatools"]`.
single_apps Apps to be included in the feature extraction computation. Use their package name, for example, `com.google.android.youtube` or the reserved word `top1global` (the most used app by a participant over the whole monitoring study).
excluded_categories App categories to be excluded in the feature extraction computation. See `APPLICATION_GENRES` in this file to add new categories or use the catalogue we provide and read `Assumtions and Observations <applications-foreground-observations>`{.interpreted-text role="ref"} for more information.
excluded_apps Apps to be excluded in the feature extraction computation. Use their package name, for example: `com.google.android.youtube`
features Features to be computed, see table below
::: {#applications-foreground-available-features}
**Available Applications Foreground Features**
:::
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 minutes 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:**
:::
Features can be computed by app, by apps grouped under a single category
(genre) and by multiple categories grouped together (meta categories).
For example, we can get features for Facebook, for Social Network Apps
(including Facebook and others) or for a meta category called Social
formed by Social Network and Social Media Tools categories.
Apps installed by default like YouTube are considered systems apps on
some phones. We do an exact match to exclude apps where \"genre\" ==
`EXCLUDED_CATEGORIES` or \"package_name\" == `EXCLUDED_APPS`.
We provide three ways of classifying and app within a category (genre):
a) by automatically scraping its official category from the Google Play
Store, b) by using the catalogue created by Stachl et al. which we
provide in RAPIDS (`data/external/`), or c) by manually creating a
personalized catalogue.
The way you choose strategy a, b or c is by modifying
`APPLICATION_GENRES` keys and values. Set `CATALOGUE_SOURCE` to `FILE`
if you want to use a CSV file as catalogue (strategy b and c) or to
`GOOGLE` if you want to scrape the genres from the Play Store (strategy
a). By default `CATALOGUE_FILE` points to the catalogue created by
Stachl et al. (strategy b) and you can change this path to your own
catalogue that follows the same format (strategy c). In addition, set
`SCRAPE_MISSING_GENRES` to true if you are using a FILE catalogue and
you want to scrape from the Play Store any missing genres and
`UPDATE_CATALOGUE_FILE` to true if you want to save those scrapped
genres back into the FILE.
The genre catalogue we provide was shared as part of the Supplemental
Materials of Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S.,
Schuwerk, T., ... Bühner, M. (2019, June 12). Behavioral Patterns in
Smartphone Usage Predict Big Five Personality Traits.
<https://doi.org/10.31234/osf.io/ks4vd>
Battery {#battery-sensor-doc}
-------
See [Battery Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L98)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:** Android and iOS
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/readable_datetime`
- Rule `rules/features.snakefile/battery_deltas`
- Rule `rules/features.snakefile/battery_features`
::: {#battery-parameters}
**Battery Rule Parameters (battery_features):**
:::
Name Description
-------------- -----------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
features Features to be computed, see table below
::: {#battery-available-features}
**Available Battery Features**
:::
Name Units Description
---------------------- ------------------ ----------------------------------------------------------------------------------------------------------------------------------------------
countdischarge episodes Number of discharging episodes.
sumdurationdischarge minutes The total duration of all discharging episodes.
countcharge episodes Number of battery charging episodes.
sumdurationcharge minutes The total duration of all charging episodes.
avgconsumptionrate episodes/minutes 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/minutes 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:**
For Aware iOS client V1 we swap battery status 3 to 5 and 1 to 3, client
V2 does not have this problem.
Activity Recognition {#activity-recognition-sensor-doc}
--------------------
See [Activity Recognition Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L90)
**Available Epochs:** daily, morning, afternoon, evening, night
**Available Platforms:** Android and iOS
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/readable_datetime`
- Rule `rules/preprocessing.snakefile/unify_ios_android`
- Rule `rules/features.snakefile/google_activity_recognition_deltas`
- Rule `rules/features.snakefile/ios_activity_recognition_deltas`
- Rule `rules/features.snakefile/activity_features`
::: {#activity-recognition-parameters}
**Rule Parameters (activity_features):**
:::
Name Description
-------------- -----------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
features Features to be computed, see table below
::: {#activity-recognition-available-features}
**Available Activity Recognition Features**
:::
Name Units Description
----------------------- ---------------- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------
count rows Number of episodes.
mostcommonactivity activity_type The most common `activity_type`. If this feature is not unique the first `activity_type` of the set of most common `activity_types` is selected ordered by `activity_type`.
countuniqueactivities activity_type Number of unique `activity_type`.
durationstationary minutes The total duration of episodes of still and tilting (phone) activities.
durationmobile minutes The total duration of episodes of on foot, running, and on bicycle activities
durationvehicle minutes The total duration of episodes of on vehicle activity
**Assumptions/Observations:**
iOS Activity Recognition data labels are unified with Google Activity
Recognition labels: \"automotive\" to \"in_vehicle\", \"cycling\" to
\"on_bicycle\", \"walking\" and \"running\" to \"on_foot\",
\"stationary\" to \"still\". In addition, iOS activity pairs formed by
\"stationary\" and \"automotive\" labels (driving but stopped at a
traffic light) are transformed to \"automotive\" only.
In AWARE, Activity Recognition data for Google (Android) and iOS are
stored in two different database tables, RAPIDS (via Snakemake)
automatically infers what platform each participant belongs to based on
their participant file (`data/external/`) which in turn takes this
information from the `aware_device` table (see `optional_ar_input`
function in `rules/features.snakefile`).
The activties are mapped to activity_types as follows:
Activity Name Activity Type
--------------- ---------------
in_vehicle 0
on_bicycle 1
on_foot 2
still 3
unknown 4
tilting 5
walking 7
running 8
Light {#light-doc}
-----
See [Light Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L112)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:** Android
**Rule Chain:**
- Rule: `rules/preprocessing.snakefile/download_dataset`
- Rule: `rules/preprocessing.snakefile/readable_datetime`
- Rule: `rules/features.snakefile/light_features`
::: {#light-parameters}
**Light Rule Parameters (light_features):**
:::
Name Description
-------------- -----------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
features Features to be computed, see table below
::: {#light-available-features}
**Available Light Features**
:::
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 (Barnett's) Features {#location-sensor-doc}
-----------------------------
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. See Ian Barnett, Jukka-Pekka
Onnela, Inferring mobility measures from GPS traces with missing data,
Biostatistics, Volume 21, Issue 2, April 2020, Pages e98--e112,
<https://doi.org/10.1093/biostatistics/kxy059>. The code for these
features was made open source by Ian Barnett
(<https://scholar.harvard.edu/ibarnett/software/gpsmobility>).
See [Location (Barnett's) Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74)
**Available Day Segments (epochs) :** only daily periods of
EVERY_DAY_INTERVAL or FLEXIBLE_DAY_INTERVAL (periods that start at
00:00:00 and end at 23:59:59 on the same day)
**Available Platforms:** Android and iOS
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset` (de
duplication and sorting by timestamp)
- Rule `rules/preprocessing.snakefile/readable_datetime` (add local
date and time components, add local day segment)
- Rule `rules/preprocessing.snakefile/phone_sensed_bins` (get the
periods of time the phone was sensing data to resample over them)
- Rule `rules/preprocessing.snakefile/process_location_types` (filter
gps data or resample fused location, deletes (0,0) coordinates)
- Rule `rules/features.snakefile/locations_r_features` (RAPIDS
executes `barnett_location_features` from
[\`src/features/location/barnett/main.R]{.title-ref})
- Rule `rules/features.snakefile/join_features_from_providers` (joins
the location features of all python and r providers)
::: {#location-parameters}
**Location Rule Parameters (location_barnett_features):**
:::
Name Description
--------------------- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are `ALL`, `GPS` 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.
minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough.
features Features to be computed, see table below
::: {#location-available-features}
**Available Location Features**
:::
Description taken from [Beiwe Summary
Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics).
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 seconds Mean duration of all flights.
stdflightdur probpause seconds The standard deviation of the duration of all flights. The fraction of a day spent in a pause (as opposed to a flight)
siglocentropy circdnrtn wkenddayrtn nats Shannon's entropy measurement based on the proportion of time spent at each significant location visited during a day. 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. Same as circdnrtn but computed separately for weekends and weekdays.
**Assumptions/Observations:**
*Types of location data to use*
Aware Android and iOS clients can collect location coordinates through
the phone\'s GPS, the network cellular towers around the phone or
Google\'s fused location API. If you want to use only the GPS provider
set `location_to_use` to `GPS`, if you want to use all providers (not
recommended due to the difference in accuracy) set `location_to_use` to
`ALL`, if your Aware client was configured to use fused location only or
want to focus only on this provider, set `location_to_use` to
`RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location
coordinates and replicates each pair forward in time as long as the
phone was sensing data as indicated by `phone_sensed_bins` (see
`Phone valid sensed days <phone-valid-sensed-days>`{.interpreted-text
role="ref"}), this is done because Google\'s API only logs a new
location coordinate pair when it is sufficiently different in time or
space from the previous one.
There are two parameters associated with resampling fused location in
the `LOCATIONS` section of the `config.yaml` file.
`RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls
the maximum gap between any two coordinate pairs to replicate the last
known pair (for example, participant A\'s phone did not collect data
between 10.30am and 10:50am and between 11:05am and 11:40am, the last
known coordinate pair will be replicated during the first period but not
the second, in other words, we assume that we cannot longer guarantee
the participant stayed at the last known location if the phone did not
sense data for more than 30 minutes).
`RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or
12 hours) makes that the last known fused location won\'t be carried
over longer that this threshold even if the phone was sensing data
continuously (for example, participant A went home at 9pm and their
phone was sensing data without gaps until 11am the next morning, the
last known location will only be replicated until 9am). If you have
suggestions to modify or improve this imputation, let us know.
*Barnett\'s et al features*
These features are based on a Pause-Flight model. A pause is defined as
a mobiity trace (location pings) within a certain duration and distance
(by default 300 seconds and 60 meters). A flight is any mobility trace
between two pauses. Data is resampled and imputed before the features
are computed. See this paper for more information:
<https://doi.org/10.1093/biostatistics/kxy059>.
In RAPIDS we only expose two parameters for these features (timezone and
accuracy). If you wish to change others you can do so in
`src/features/location/barnett/library/MobilityFeatures.R`
*Significant Locations*
Significant locations are determined using K-means clustering on pauses
longer than 10 minutes. The number of clusters (K) is increased until no
two clusters are within 400 meters from each other. After this, pauses
within a certain range of a cluster (200 meters by default) will count
as a visit to that significant location. This description was adapted
from the Supplementary Materials of
<https://doi.org/10.1093/biostatistics/kxy059>.
*The Circadian Calculation*
For a detailed description of how this is calculated, see Canzian, L., &
Musolesi, M. (2015, September). Trajectories of depression: unobtrusive
monitoring of depressive states by means of smartphone mobility traces
analysis. In Proceedings of the 2015 ACM international joint conference
on pervasive and ubiquitous computing (pp. 1293-1304). Their procedure
was followed using 30-min increments as a bin size. Taken from [Beiwe
Summary Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics).
Location (Doryab\'s) Features
-----------------------------
Doryab\'s location features are based on this paper: Doryab, A.,
Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral
Features from Smartphone and Wearable Data. ArXiv:1812.10394 \[Cs,
Stat\]. <http://arxiv.org/abs/1812.10394>
See [Location (Doryab\'s) Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74)
**Available Day Segments (epochs):** any of EVERY_DAY_FREQUENCY,
EVERY_DAY_INTERVAL and FLEXIBLE_DAY_INTERVAL
**Available Platforms:** Android and iOS
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset` (de
duplication and sorting by timestamp)
- Rule `rules/preprocessing.snakefile/readable_datetime` (add local
date and time components, add local day segment)
- Rule `rules/preprocessing.snakefile/phone_sensed_bins` (get the
periods of time the phone was sensing data to resample over them)
- Rule `rules/preprocessing.snakefile/process_location_types` (filter
gps data or resample fused location, deletes (0,0) coordinates)
- Rule `rules/features.snakefile/locations_python_features` (RAPIDS
executes `doryab_location_features` from
[\`src/features/location/doryab/main.py]{.title-ref})
- Rule `rules/features.snakefile/join_features_from_providers` (joins
the location features of all python and r providers)
::: {#location-doryab-parameters}
**Location Rule Parameters (location_doryab_features):**
:::
Name Description
----------------------- -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are `ALL`, `GPS` OR `RESAMPLE_FUSED`.
features Features to be computed, see table below.
threshold_static It is the threshold value in km/hr which labels a row as Static or Moving.
dbscan_minsamples The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself.
dbscan_eps The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function.
maximum_gap_allowed The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing.
minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough.
sampling_frequency Expected time difference between any two location rows in minutes. If set to \'0\', the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps. This parameter impacts all the time calculations.
::: {#location-doryab-available-features}
**Available Location Features**
:::
Name Units Description
------------------------------------------------------------ ------------- -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
locationvariance loglocationvariance $meters^2$ The sum of the variances of the latitude and longitude columns. Log of the sum of the variances of the latitude and longitude columns.
totaldistance meters Total distance travelled in a `day_segment` using the haversine formula.
averagespeed km/hr Average speed in a `day_segment` considering only the instances labeled as Moving.
varspeed circadianmovement km/hr Speed variance in a `day_segment` considering only the instances labeled as Moving. \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" (Doryab et. al. 2019)
numberofsignificantplaces places Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as paramters to identify clusters. Each cluster is a significant place.
numberlocationtransitions transitions Number of movements between any two clusters in a `day_segment`.
radiusgyration meters Quantifies the area covered by a participant
timeattop1location minutes Time spent at the most significant location.
timeattop2location minutes Time spent at the 2nd most significant location.
timeattop3location movingtostaticratio outlierstimepercent minutes Time spent at the 3rd most significant location. Ratio between the number of rows labeled Moving versus Static Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a `day_segment`.
maxlengthstayatclusters minutes Maximum time spent in a cluster (significant location).
minlengthstayatclusters minutes Minimum time spent in a cluster (significant location).
meanlengthstayatclusters minutes Average time spent in a cluster (significant location).
stdlengthstayatclusters minutes Standard deviation of time spent in a cluster (significant location).
locationentropy nats Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location).
normalizedlocationentropy nats Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location).
**Assumptions/Observations:**
*Types of location data to use*
Aware Android and iOS clients can collect location coordinates through
the phone\'s GPS, the network cellular towers around the phone or
Google\'s fused location API. If you want to use only the GPS provider
set `location_to_use` to `GPS`, if you want to use all providers (not
recommended due to the difference in accuracy) set `location_to_use` to
`ALL`, if your Aware client was configured to use fused location only or
want to focus only on this provider, set `location_to_use` to
`RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location
coordinates and replicates each pair forward in time as long as the
phone was sensing data as indicated by `phone_sensed_bins` (see
`Phone valid sensed days <phone-valid-sensed-days>`{.interpreted-text
role="ref"}), this is done because Google\'s API only logs a new
location coordinate pair when it is sufficiently different in time or
space from the previous one.
There are two parameters associated with resampling fused location in
the `LOCATIONS` section of the `config.yaml` file.
`RESAMPLE_FUSED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls
the maximum gap between any two coordinate pairs to replicate the last
known pair (for example, participant A\'s phone did not collect data
between 10.30am and 10:50am and between 11:05am and 11:40am, the last
known coordinate pair will be replicated during the first period but not
the second, in other words, we assume that we cannot longer guarantee
the participant stayed at the last known location if the phone did not
sense data for more than 30 minutes).
`RESAMPLE_FUSED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or
12 hours) makes that the last known fused location won\'t be carried
over longer that this threshold even if the phone was sensing data
continuously (for example, participant A went home at 9pm and their
phone was sensing data without gaps until 11am the next morning, the
last known location will only be replicated until 9am). If you have
suggestions to modify or improve this imputation, let us know.
*Significant Locations Identified*
Significant locations are determined using DBSCAN clustering on
locations that a patient visit over the course of the period of data
collection.
*Circadian Movement Calculation*
\"Circadian movement (Saeb et al. 2015) is calculated using the
Lomb-Scargle method\" (Doryab et. al. 2019)
Screen {#screen-sensor-doc}
------
See [Screen Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L104)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:** Android and iOS
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/readable_datetime`
- Rule `rules/preprocessing.snakefile/unify_ios_android`
- Rule `rules/features.snakefile/screen_deltas`
- Rule `rules/features.snakefile/screen_features`
::: {#screen-parameters}
**Screen Rule Parameters (screen_features):**
:::
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
ignore_episodes_shorter_than Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter.
ignore_episodes_longer_than Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter.
features_deltas Features to be computed, see table below
episode_types Currently we only support unlock episodes (from when the phone is unlocked until the screen is off)
::: {#screen-episodes-available-features}
**Available Screen Episodes Features**
:::
Name Units Description
------------------------- ----------------- ----------------------------------------------------------------------------------------------------------------------------
sumduration minutes Total duration of all unlock episodes.
maxduration minutes Longest duration of any unlock episode.
minduration minutes Shortest duration of any unlock episode.
avgduration minutes Average duration of all unlock episodes.
stdduration minutes 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 minutes Minutes until the first unlock episode.
**Assumptions/Observations:**
In Android, `lock` events can happen right after an `off` event, after a
few seconds of an `off` event, or never happen depending on the phone\'s
settings, therefore, an `unlock` episode is defined as the time between
an `unlock` and a `off` event. In iOS, `on` and `off` events do not
exist, so an `unlock` episode is defined as the time between an `unlock`
and a `lock` event.
Events in iOS are recorded reliably albeit some duplicated `lock` events
within milliseconds from each other, so we only keep consecutive
unlock/lock pairs. In Android you cand find multiple consecutive
`unlock` or `lock` events, so we only keep consecutive unlock/off pairs.
In our experiments these cases are less than 10% of the screen events
collected and this happens because `ACTION_SCREEN_OFF` and
`ACTION_SCREEN_ON` are \"sent when the device becomes non-interactive
which may have nothing to do with the screen turning off\". In addition
to unlock/off episodes, in Android it is possible to measure the time
spent on the lock screen before an `unlock` event as well as the total
screen time (i.e. `ON` to `OFF`) but these are not implemented at the
moment.
To unify the screen processing and use the same code in RAPIDS, we
replace LOCKED episodes with OFF episodes (2 with 0) in iOS. However, as
mentioned above this is still computing `unlock` to `lock` episodes.
Conversation {#conversation-sensor-doc}
------------
See [Conversation Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L191)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:** Android and iOS
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/readable_datetime`
- Rule `rules/features.snakefile/conversation_features`
::: {#conversation-parameters}
**Conversation Rule Parameters (conversation_features):**
:::
Name Description
------------------ ------------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segments` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
recordingMinutes Minutes the plugin was recording audio (default 1 min)
pausedMinutes Minutes the plugin was NOT recording audio (default 3 min)
features Features to be computed, see table below
::: {#conversation-available-features}
**Available Conversation Features**
:::
Name Units Description
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ --------- ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
minutessilence minutes Minutes labeled as silence
minutesnoise minutes Minutes labeled as noise
minutesvoice minutes Minutes labeled as voice
minutesunknown minutes Minutes labeled as unknown
sumconversationduration minutes Total duration of all conversations
maxconversationduration minutes Longest duration of all conversations
minconversationduration minutes Shortest duration of all conversations
avgconversationduration minutes Average duration of all conversations
sdconversationduration minutes Standard Deviation of the duration of all conversations
timefirstconversation minutes Minutes since midnight when the first conversation for a day segment was detected
timelastconversation minutes Minutes since midnight when the last conversation for a day segment was detected
noisesumenergy L2-norm Sum of all energy values when inference is noise
noiseavgenergy L2-norm Average of all energy values when inference is noise
noisesdenergy L2-norm Standard Deviation of all energy values when inference is noise
noiseminenergy L2-norm Minimum of all energy values when inference is noise
noisemaxenergy L2-norm Maximum of all energy values when inference is noise
voicesumenergy L2-norm Sum of all energy values when inference is voice
voiceavgenergy L2-norm Average of all energy values when inference is voice
voicesdenergy L2-norm Standard Deviation of all energy values when inference is voice
voiceminenergy L2-norm Minimum of all energy values when inference is voice
voicemaxenergy silencesensedfraction noisesensedfraction voicesensedfraction unknownsensedfraction silenceexpectedfraction noiseexpectedfraction voiceexpectedfraction unknownexpectedfraction L2-norm Maximum of all energy values when inference is voice Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes)
**Assumptions/Observations:** N/A
Fitbit: Sleep {#fitbit-sleep-sensor-doc}
-------------
See [Fitbit: Sleep Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L165)
**Available Epochs (day_segment) :** daily
**Available Platforms:**: Fitbit
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/fitbit_with_datetime`
- Rule `rules/features.snakefile/fitbit_sleep_features`
::: {#fitbit-sleep-parameters}
**Fitbit: Sleep Rule Parameters (fitbit_sleep_features):**
:::
Name Description
-------------------------------------- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. For this sensor only `daily` is used.
sleep_types The types of sleep provided by Fitbit: `main`, `nap`, `all`.
daily_features_from_summary_data The sleep features that can be computed based on Fitbit\'s summary data. See `Available Fitbit: Sleep Features <fitbit-sleep-available-features>`{.interpreted-text role="ref"} Table below
::: {#fitbit-sleep-available-features}
**Available Fitbit: Sleep Features**
:::
Name Units Description
------------------------- ---------- --------------------------------------------------------------------------------------------------------------------------------------------------------------------
sumdurationtofallasleep minutes Time it took the user to fall asleep for `sleep_type` during `day_segment`.
sumdurationawake minutes Time the user was awake but still in bed for `sleep_type` during `day_segment`.
sumdurationasleep minutes Sleep duration for `sleep_type` during `day_segment`.
sumdurationafterwakeup minutes Time the user stayed in bed after waking up for `sleep_type` during `day_segment`.
sumdurationinbed minutes Total time the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for `sleep_type` during `day_segment`.
avgefficiency scores Sleep efficiency average for `sleep_type` during `day_segment`.
countepisode episodes Number of sleep episodes for `sleep_type` during `day_segment`.
**Assumptions/Observations:**
Only features from summary data are available at the momement.
The [fitbit_with_datetime]{.title-ref} rule will extract Summary data
([fitbit_sleep_summary_with_datetime.csv]{.title-ref}) and Intraday
data ([fitbit_sleep_intraday_with_datetime.csv]{.title-ref}). There
are two versions of Fitbit\'s sleep API ([version
1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and
[version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)),
and each provides raw sleep data in a different format:
> - Sleep level. In `v1`, sleep level is an integer with three
> possible values (1, 2, 3) while in `v1.2` is a string. We convert
> integer levels to strings, `asleep`, `restless` or `awake`
> respectively.
> - Count summaries. For Summary data, `v1` contains `count_awake`,
> `duration_awake`, `count_awakenings`, `count_restless`, and
> `duration_restless` fields for every sleep record while `v1.2`
> does not.
> - Types of sleep records. `v1.2` has two types of sleep records:
> `classic` and `stages`. The `classic` type contains three sleep
> levels: `awake`, `restless` and `asleep`. The `stages` type
> contains four sleep levels: `wake`, `deep`, `light`, and `rem`.
> Sleep records from `v1` will have the same sleep levels as
> [v1.2]{.title-ref} classic type; therefore we set their type to
> `classic`.
> - Unified level of sleep. For intraday data, we unify sleep levels
> of each sleep record with a column named `unified_level`. Based on
> [this Fitbit forum
> post](https://community.fitbit.com/t5/Alta/What-does-Restless-mean-in-sleep-tracking/td-p/2989011)
> , we merge levels into two categories:
> - For the `classic` type unified_level is one of {0, 1} where 0
> means awake and groups `awake` + `restless`, while 1 means
> asleep and groups `asleep`.
> - For the `stages` type, unified_level is one of {0, 1} where 0
> means awake and groups `wake` while 1 means asleep and groups
> `deep` + `light` + `rem`.
> - Short Data. In `v1.2`, records of type `stages` contain
> `shortData` in addition to `data`. We merge both to extract
> intraday data.
> - `data` contains sleep stages and any wake periods \> 3 minutes
> (180 seconds).
> - `shortData` contains short wake periods representing
> physiological awakenings that are \<= 3 minutes (180 seconds).
> - The following columns of Summary data are not computed by RAPIDS
> but taken directly from columns with a similar name provided by
> Fitbit\'s API: `efficiency`, `minutes_after_wakeup`,
> `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`,
> `minutes_in_bed`, `is_main_sleep` and `type`
> - The following columns of Intraday data are not computed by RAPIDS
> but taken directly from columns with a similar name provided by
> Fitbit\'s API: `original_level`, `is_main_sleep` and `type`. We
> compute `unified_level` as explained above.
These are examples of intraday and summary data:
- Intraday data (at 30-second intervals for `stages` type or 60-second
intervals for `classic` type)
device_id original_level unified_level is_main_sleep type local_date_time local_date local_month local_day local_day_of_week local_time local_hour local_minute local_day_segment
------------ ----------------- ---------------- ----------------- -------- --------------------- ------------- -------------- ------------ ---------------------- ------------- ------------- --------------- ---------------------
did wake 0 1 stages 2020-05-20 22:13:30 2020-05-20 5 20 2 22:13:30 22 13 evening
did wake 0 1 stages 2020-05-20 22:14:00 2020-05-20 5 20 2 22:14:00 22 14 evening
did light 1 1 stages 2020-05-20 22:14:30 2020-05-20 5 20 2 22:14:30 22 14 evening
did light 1 1 stages 2020-05-20 22:15:00 2020-05-20 5 20 2 22:15:00 22 15 evening
did light 1 1 stages 2020-05-20 22:15:30 2020-05-20 5 20 2 22:15:30 22 15 evening
- Summary data
device_id efficiency minutes_after_wakeup minutes_asleep minutes_awake minutes_to_fall_asleep minutes_in_bed is_main_sleep type local_start_date_time local_end_date_time local_start_date local_end_date local_start_day_segment local_end_day_segment
------------ ------------ ------------------------ ----------------- ---------------- --------------------------- ------------------ ----------------- -------- -------------------------- ------------------------ -------------------- ------------------ ---------------------------- --------------------------
did 90 0 381 54 0 435 1 stages 2020-05-20 22:12:00 2020-05-21 05:27:00 2020-05-20 2020-05-21 evening night
did 88 0 498 86 0 584 1 stages 2020-05-22 22:03:00 2020-05-23 07:47:03 2020-05-22 2020-05-23 evening morning
Fitbit: Heart Rate {#fitbit-heart-rate-sensor-doc}
------------------
See [Fitbit: Heart Rate Config
Code](https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L141)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:**: Fitbit
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/fitbit_with_datetime`
- Rule `rules/features.snakefile/fitbit_heartrate_features`
::: {#fitbit-heart-rate-parameters}
**Fitbit: Heart Rate Rule Parameters (fitbit_heartrate_features):**
:::
Name Description
-------------- ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
features The heartrate features that can be computed. See `Available Fitbit: Heart Rate Features <fitbit-heart-rate-available-features>`{.interpreted-text role="ref"} Table below
::: {#fitbit-heart-rate-available-features}
**Available Fitbit: Heart Rate Features**
:::
Name Units Description
------------------ ------------ --------------------------------------------------------------------------------------------------------------
restingheartrate beats/mins The number of times your heart beats per minute when participant is still and well rested for `daily` epoch.
calories cals Calories burned during `heartrate_zone` for `daily` epoch.
maxhr beats/mins The maximum heart rate during `day_segment` epoch.
minhr beats/mins The minimum heart rate during `day_segment` epoch.
avghr beats/mins The average heart rate during `day_segment` epoch.
medianhr beats/mins The median of heart rate during `day_segment` epoch.
modehr beats/mins The mode of heart rate during `day_segment` epoch.
stdhr beats/mins The standard deviation of heart rate during `day_segment` epoch.
diffmaxmodehr beats/mins The difference between the maximum and mode heart rate during `day_segment` epoch.
diffminmodehr beats/mins The difference between the mode and minimum heart rate during `day_segment` epoch.
entropyhr nats Shannon's entropy measurement based on heart rate during `day_segment` epoch.
minutesonZONE minutes Number of minutes the user\'s heartrate fell within each `heartrate_zone` during `day_segment` epoch.
**Assumptions/Observations:**
There are four heart rate zones: `out_of_range`, `fat_burn`, `cardio`,
and `peak`. Please refer to [Fitbit
documentation](https://help.fitbit.com/articles/en_US/Help_article/1565)
for more information about the way they are computed.
Calories\' accuracy depends on the users' Fitbit profile (weight,
height, etc.).
Fitbit: Steps {#fitbit-steps-sensor-doc}
-------------
See [Fitbit: Steps Config
Code](https://github.com/carissalow/rapids/blob/29b04b0601b62379fbdb76de685f3328b8dde2a2/config.yaml#L148)
**Available Epochs (day_segment) :** daily, morning, afternoon,
evening, night
**Available Platforms:**: Fitbit
**Snakemake rule chain:**
- Rule `rules/preprocessing.snakefile/download_dataset`
- Rule `rules/preprocessing.snakefile/fitbit_with_datetime`
- Rule `rules/features.snakefile/fitbit_step_features`
::: {#fitbit-steps-parameters}
**Fitbit: Steps Rule Parameters (fitbit_step_features):**
:::
Name Description
------------------------------ ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
day_segment The particular `day_segment` that will be analyzed. The available options are `daily`, `morning`, `afternoon`, `evening`, `night`
features The features that can be computed. See `Available Fitbit: Steps Features <fitbit-steps-available-features>`{.interpreted-text role="ref"} Table below
threshold_active_bout Every minute with Fitbit step data wil be labelled as `sedentary` if its step count is below this threshold, otherwise, `active`.
include_zero_step_rows Whether or not to include day segments with a 0 step count
exclude_sleep Whether or not to exclude step rows that happen during sleep
exclude_sleep_type If `exclude_sleep` is True, then you can choose between `FIXED` or `FITBIT_BASED`. `FIXED` will exclude all step rows that happen between a start and end time (see below). `FITBIT_BASED` will exclude step rows that happen during main sleep segments as measured by the Fitbit device (`config[SLEEP][DB_TABLE]` should be a valid table in your database, it usually is the same table that contains your STEP data)
exclude_sleep_fixed_start Start time of the fixed sleep period to exclude. Only relevant if `exclude_sleep` is True and `exclude_sleep_type` is `FIXED`
exclude_sleep_fixed_end Start time of the fixed sleep period to exclude. Only relevant if `exclude_sleep` is True and `exclude_sleep_type` is `FIXED`
::: {#fitbit-steps-available-features}
**Available Fitbit: Steps Features**
:::
Name Units Description
--------------------------- --------- ---------------------------------------------------------------------------------------
sumallsteps steps The total step count during `day_segment` epoch.
maxallsteps steps The maximum step count during `day_segment` epoch.
minallsteps steps The minimum step count during `day_segment` epoch.
avgallsteps steps The average step count during `day_segment` epoch.
stdallsteps steps The standard deviation of step count during `day_segment` epoch.
countepisodesedentarybout bouts Number of sedentary bouts during `day_segment` epoch.
sumdurationsedentarybout minutes Total duration of all sedentary bouts during `day_segment` epoch.
maxdurationsedentarybout minutes The maximum duration of any sedentary bout during `day_segment` epoch.
mindurationsedentarybout minutes The minimum duration of any sedentary bout during `day_segment` epoch.
avgdurationsedentarybout minutes The average duration of sedentary bouts during `day_segment` epoch.
stddurationsedentarybout minutes The standard deviation of the duration of sedentary bouts during `day_segment` epoch.
countepisodeactivebout bouts Number of active bouts during `day_segment` epoch.
sumdurationactivebout minutes Total duration of all active bouts during `day_segment` epoch.
maxdurationactivebout minutes The maximum duration of any active bout during `day_segment` epoch.
mindurationactivebout minutes The minimum duration of any active bout during `day_segment` epoch.
avgdurationactivebout minutes The average duration of active bouts during `day_segment` epoch.
stddurationactivebout minutes The standard deviation of the duration of active bouts during `day_segment` epoch.
**Assumptions/Observations:**
Active and sedentary bouts. If the step count per minute is smaller than
`THRESHOLD_ACTIVE_BOUT` (default value is 10), that minute is labelled
as sedentary, otherwise, is labelled as active. Active and sedentary
bouts are periods of consecutive minutes labelled as `active` or
`sedentary`.
`validsensedminutes` feature is not available for Step sensor as we
cannot determine the valid minutes based on the raw Fitbit step data.