# See https://www.rapids.science/latest/setup/configuration/#database-credentials DATABASE_GROUP: &database_group MY_GROUP # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study TIMEZONE: &timezone America/New_York # See https://www.rapids.science/latest/setup/configuration/#participant-files PIDS: [test01, test02, test03, test04] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: SOURCE: TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE DATABASE_GROUP: *database_group CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format TIMEZONE: *timezone PHONE_SECTION: ADD: TRUE DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] FITBIT_SECTION: ADD: TRUE DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] # See https://www.rapids.science/latest/setup/configuration/#time-segments TIME_SEGMENTS: &time_segments TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT FILE: "data/external/timesegments_periodic.csv" INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs ######################################################################################################################## # PHONE # ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration PHONE_DATA_CONFIGURATION: SOURCE: TYPE: DATABASE DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: TYPE: SINGLE VALUE: *timezone # Sensors ------ # https://www.rapids.science/latest/features/phone-accelerometer/ PHONE_ACCELEROMETER: TABLE: accelerometer PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer SRC_LANGUAGE: "python" PANDA: COMPUTE: False VALID_SENSED_MINUTES: False FEATURES: exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] SRC_FOLDER: "panda" # inside src/features/phone_accelerometer SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-activity-recognition/ PHONE_ACTIVITY_RECOGNITION: TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] ACTIVITY_CLASSES: STATIONARY: ["still", "tilting"] MOBILE: ["on_foot", "walking", "running", "on_bicycle"] VEHICLE: ["in_vehicle"] SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-applications-crashes/ PHONE_APPLICATIONS_CRASHES: TABLE: applications_crashes APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-applications-foreground/ PHONE_APPLICATIONS_FOREGROUND: TABLE: applications_foreground APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: RAPIDS: COMPUTE: False SINGLE_CATEGORIES: ["all", "email"] MULTIPLE_CATEGORIES: social: ["socialnetworks", "socialmediatools"] entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps EXCLUDED_CATEGORIES: [] EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-applications-notifications/ PHONE_APPLICATIONS_NOTIFICATIONS: TABLE: applications_notifications APPLICATION_CATEGORIES: CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-aware-log/ PHONE_AWARE_LOG: TABLE: aware_log PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-battery/ PHONE_BATTERY: TABLE: battery EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] SRC_FOLDER: "rapids" # inside src/features/phone_battery SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-bluetooth/ PHONE_BLUETOOTH: TABLE: bluetooth PROVIDERS: RAPIDS: COMPUTE: TRUE FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth SRC_LANGUAGE: "r" DORYAB: COMPUTE: FALSE FEATURES: ALL: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] OWN: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] OTHERS: DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"] SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"] SRC_FOLDER: "doryab" # inside src/features/phone_bluetooth SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-calls/ PHONE_CALLS: TABLE: calls PROVIDERS: RAPIDS: COMPUTE: TRUE CALL_TYPES: [missed, incoming, outgoing] FEATURES: missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] SRC_LANGUAGE: "r" SRC_FOLDER: "rapids" # inside src/features/phone_calls # See https://www.rapids.science/latest/features/phone-conversation/ PHONE_CONVERSATION: TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] RECORDING_MINUTES: 1 PAUSED_MINUTES : 3 SRC_FOLDER: "rapids" # inside src/features/phone_conversation SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-data-yield/ PHONE_DATA_YIELD: SENSORS: [] PROVIDERS: RAPIDS: COMPUTE: False FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least SRC_LANGUAGE: "r" SRC_FOLDER: "rapids" # inside src/features/phone_data_yield # See https://www.rapids.science/latest/features/phone-keyboard/ PHONE_KEYBOARD: TABLE: keyboard PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-light/ PHONE_LIGHT: TABLE: light PROVIDERS: RAPIDS: COMPUTE: FALSE FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] SRC_FOLDER: "rapids" # inside src/features/phone_light SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-locations/ PHONE_LOCATIONS: TABLE: locations LOCATIONS_TO_USE: ALL_RESAMPLED # ALL, GPS, ALL_RESAMPLED, OR FUSED_RESAMPLED FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row PROVIDERS: DORYAB: COMPUTE: False FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius DBSCAN_EPS: 10 # meters DBSCAN_MINSAMPLES: 5 THRESHOLD_STATIC : 1 # km/h MAXIMUM_GAP_ALLOWED: 300 MINUTES_DATA_USED: False SAMPLING_FREQUENCY: 0 CLUSTER_ON: PARTICIPANT_DATASET # PARTICIPANT_DATASET,TIME_SEGMENT CLUSTERING_ALGORITHM: DBSCAN #DBSCAN,OPTICS SRC_FOLDER: "doryab" # inside src/features/phone_locations SRC_LANGUAGE: "python" BARNETT: COMPUTE: False FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius TIMEZONE: *timezone MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features SRC_FOLDER: "barnett" # inside src/features/phone_locations SRC_LANGUAGE: "r" # See https://www.rapids.science/latest/features/phone-messages/ PHONE_MESSAGES: TABLE: messages PROVIDERS: RAPIDS: COMPUTE: TRUE MESSAGES_TYPES : [received, sent] FEATURES: received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] SRC_LANGUAGE: "r" SRC_FOLDER: "rapids" # inside src/features/phone_messages # See https://www.rapids.science/latest/features/phone-screen/ PHONE_SCREEN: TABLE: screen PROVIDERS: RAPIDS: COMPUTE: False REFERENCE_HOUR_FIRST_USE: 0 IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later EPISODE_TYPES: ["unlock"] SRC_FOLDER: "rapids" # inside src/features/phone_screen SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/phone-wifi-connected/ PHONE_WIFI_CONNECTED: TABLE: "sensor_wifi" PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected SRC_LANGUAGE: "r" # See https://www.rapids.science/latest/features/phone-wifi-visible/ PHONE_WIFI_VISIBLE: TABLE: "wifi" PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible SRC_LANGUAGE: "r" ######################################################################################################################## # FITBIT # ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration FITBIT_DATA_CONFIGURATION: SOURCE: TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT DATABASE_GROUP: *database_group DEVICE_ID_COLUMN: device_id # column name TIMEZONE: TYPE: SINGLE # Fitbit devices don't support time zones so we read this data in the timezone indicated by VALUE VALUE: *timezone # Sensors ------ # See https://www.rapids.science/latest/features/fitbit-heartrate-summary/ FITBIT_HEARTRATE_SUMMARY: TABLE: heartrate_summary PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/fitbit-heartrate-intraday/ FITBIT_HEARTRATE_INTRADAY: TABLE: heartrate_intraday PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/fitbit-sleep-summary/ FITBIT_SLEEP_SUMMARY: TABLE: sleep_summary SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] SLEEP_TYPES: ["main", "nap", "all"] SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/fitbit-steps-summary/ FITBIT_STEPS_SUMMARY: TABLE: steps_summary PROVIDERS: RAPIDS: COMPUTE: False FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/fitbit-steps-intraday/ FITBIT_STEPS_INTRADAY: TABLE: steps_intraday PROVIDERS: RAPIDS: COMPUTE: False FEATURES: STEPS: ["sum", "max", "min", "avg", "std"] SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] THRESHOLD_ACTIVE_BOUT: 10 # steps INCLUDE_ZERO_STEP_ROWS: False SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday SRC_LANGUAGE: "python" # FITBIT_CALORIES: # TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES # TABLE: # JSON: fitbit_calories # CSV: # SUMMARY: calories_summary # INTRADAY: calories_intraday # PROVIDERS: # RAPIDS: # COMPUTE: False # FEATURES: [] ######################################################################################################################## # PLOTS # ######################################################################################################################## # Data quality HISTOGRAM_PHONE_DATA_YIELD: PLOT: False HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: PLOT: False HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: PLOT: False HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: PLOT: False SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] # Features HEATMAP_FEATURE_CORRELATION_MATRIX: PLOT: False MIN_ROWS_RATIO: 0.5 CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}