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Datasets
Data Types
Feature Data

Feature Data

Data Files

The FeatureData folder contains seven files, all index by pid and date.

  • rapids.csv: The complete feature file that contains all features.
  • location.csv: The feature file that contains all Location features.
  • screen.csv: The feature file that contains all PhoneUsage features.
  • call.csv: The feature file that contains all Call features.
  • bluetooth.csv: The feature file that contains all Bluetooth features.
  • steps.csv: The feature file that contains all PhysicalActivity features.
  • sleep.csv: The feature file that contains all Sleep features.
  • wifi.csv: The feature file that contains all WiFi features. Note that this feature type is not used by any existing algorithms and often has a high data missing rate.

Processing

Our dataset was collected using an app based on the AWARE framework. We then employed RAPIDS for feature extraction. Our feature extraction contains several processing steps.

  • All features are extracted with multiple time_segments
    • morning (6 am - 12 pm, calculated daily)
    • afternoon (12 pm - 6 pm, calculated daily)
    • evening (6 pm - 12 am, calculated daily)
    • night (12 am - 6 am, calculated daily)
    • allday (24 hrs from 12 am to 11:59 pm, calculated daily)
    • 7-day history (calculated daily)
    • 14-day history (calculated daily)
    • weekdays (calculated once per week on Friday)
    • weekend (calculated once per week on Sunday)
  • For all features with numeric values, we also provide two more versions:
    • normalized: subtracted by each participant's median and divided by the 5-95 quantile range
    • discretized: low/medium/high split by 33/66 quantile of each participant's feature value

Naming Format

All features follow a consistent naming format: [feature_type]:[feature_name][version]:[time_segment]

  • feature_type: It corresponds to the six data types. location - f_loc, screen - f_screen, call - f_call, bluetooth - f_blue, steps - f_steps, sleep - f_slp.
  • feature_name: The name of the feature provided by RAPIDS, i.e., the second column of the following figure, plus some additional information. A typical format is [SensorType]_[CodeProvider]_[featurename]. Please refer to RAPIDS's naming format for more details.
  • version: It has three versions: 1) nothing, just empty ""; 2) normalized, _norm; 3) discretized, _dis.
  • time_segment: It corresponds to the specific time segment. morning - morning, afternoon - afternoon, evening - evening, night - night, allday - allday, 7-day history - 7dhist, 14-day history - 14dhist, weekday - weekday, weekend - weekend.

A participant's sumdurationunlock normalized feature in mornings is f_loc:phone_screen_rapids_sumdurationunlock_norm:morning.

Feature Details

Please find the following pages about different feature type available in our datasets. Most texts are taken from RAPIDS with courtesy.

EMA Surveys
Location