######################################################################################################################## # GLOBAL CONFIGURATION # ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#participant-files PIDS: [test01] # See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files CREATE_PARTICIPANT_FILES: CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format PHONE_SECTION: ADD: True DEVICE_ID_COLUMN: device_id # column name IGNORED_DEVICE_IDS: [] FITBIT_SECTION: ADD: True DEVICE_ID_COLUMN: fitbit_id # column name IGNORED_DEVICE_IDS: [] EMPATICA_SECTION: ADD: True DEVICE_ID_COLUMN: empatica_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 # See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study TIMEZONE: TYPE: SINGLE SINGLE: TZCODE: America/New_York MULTIPLE: TZCODES_FILE: data/external/multiple_timezones_example.csv IF_MISSING_TZCODE: STOP DEFAULT_TZCODE: America/New_York FITBIT: ALLOW_MULTIPLE_TZ_PER_DEVICE: False INFER_FROM_SMARTPHONE_TZ: False ######################################################################################################################## # PHONE # ######################################################################################################################## # See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration PHONE_DATA_STREAMS: USE: aware_mysql # AVAILABLE: aware_mysql: DATABASE_GROUP: MY_GROUP aware_csv: FOLDER: data/external/aware_csv # 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 AR 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-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: False 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: False 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, minimum percentage of valid minutes in an hour to be considered valid. 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 HOME_INFERENCE: DBSCAN_EPS: 10 # meters DBSCAN_MINSAMPLES: 5 THRESHOLD_STATIC : 1 # km/h CLUSTERING_ALGORITHM: DBSCAN #DBSCAN,OPTICS PROVIDERS: DORYAB: COMPUTE: False FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed", "numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","timeathome"] 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_ROW_GAP: 300 MAXIMUM_ROW_DURATION: 60 MINUTES_DATA_USED: False CLUSTER_ON: PARTICIPANT_DATASET # PARTICIPANT_DATASET,TIME_SEGMENT CLUSTERING_ALGORITHM: DBSCAN #DBSCAN,OPTICS RADIUS_FOR_HOME: 100 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 IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON 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-log/ PHONE_LOG: TABLE: ANDROID: aware_log IOS: ios_aware_log PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD # See https://www.rapids.science/latest/features/phone-messages/ PHONE_MESSAGES: TABLE: messages PROVIDERS: RAPIDS: COMPUTE: False 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_STREAMS: USE: fitbitjson_mysql # AVAILABLE: fitbitjson_mysql: DATABASE_GROUP: MY_GROUP SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. fitbitparsed_mysql: DATABASE_GROUP: MY_GROUP SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. fitbitjson_csv: FOLDER: data/external/fitbit_csv SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. fitbitparsed_csv: FOLDER: data/external/fitbit_csv SLEEP_SUMMARY_EPISODE_DAY_ANCHOR: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. # Sensors ------ # See https://www.rapids.science/latest/features/fitbit-data-yield/ FITBIT_DATA_YIELD: SENSOR: FITBIT_HEARTRATE_INTRADAY PROVIDERS: RAPIDS: COMPUTE: False FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid. SRC_LANGUAGE: "r" SRC_FOLDER: "rapids" # inside src/features/fitbit_data_yield # 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 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-sleep-intraday/ FITBIT_SLEEP_INTRADAY: TABLE: sleep_intraday PROVIDERS: RAPIDS: COMPUTE: False FEATURES: LEVELS_AND_TYPES_COMBINING_ALL: True LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration] RATIOS_TYPE: [count, duration] RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES] ROUTINE: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap] SLEEP_LEVELS: CLASSIC: [awake, restless, asleep] STAGES: [wake, deep, light, rem] UNIFIED: [awake, asleep] SLEEP_TYPES: [main, nap] INCLUDE_SLEEP_LATER_THAN: 0 # a number ranged from 0 (midnight) to 1439 (23:59) REFERENCE_TIME: MIDNIGHT # chosen from "MIDNIGHT" and "START_OF_THE_SEGMENT" SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_intraday SRC_LANGUAGE: "python" PRICE: COMPUTE: False FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, "stdstarttimeofepisodemain", "stdendtimeofepisodemain", "stdmidpointofepisodemain", socialjetlag, meanssdstarttimeofepisodemain, meanssdendtimeofepisodemain, meanssdmidpointofepisodemain, medianssdstarttimeofepisodemain, medianssdendtimeofepisodemain, medianssdmidpointofepisodemain] SLEEP_LEVELS: CLASSIC: [awake, restless, asleep] STAGES: [wake, deep, light, rem] UNIFIED: [awake, asleep] DAY_TYPES: [WEEKEND, WEEK, ALL] GROUP_EPISODES_WITHIN: # by default: today's 6pm to tomorrow's noon START_TIME: 1080 # number of minutes after the midnight (18:00) 18*60 LENGTH: 1080 # in minutes (18 hours) 18*60 SRC_FOLDER: "price" # inside src/features/fitbit_sleep_intraday 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" ######################################################################################################################## # EMPATICA # ######################################################################################################################## EMPATICA_DATA_STREAMS: USE: empatica_zip # AVAILABLE: empatica_zip: FOLDER: data/external/empatica # Sensors ------ # See https://www.rapids.science/latest/features/empatica-accelerometer/ EMPATICA_ACCELEROMETER: TABLE: ACC PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] SRC_FOLDER: "dbdp" # inside src/features/empatica_accelerometer SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/empatica-heartrate/ EMPATICA_HEARTRATE: TABLE: HR PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr"] SRC_FOLDER: "dbdp" # inside src/features/empatica_heartrate SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/empatica-temperature/ EMPATICA_TEMPERATURE: TABLE: TEMP PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"] SRC_FOLDER: "dbdp" # inside src/features/empatica_heartrate SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/ EMPATICA_ELECTRODERMAL_ACTIVITY: TABLE: EDA PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"] SRC_FOLDER: "dbdp" # inside src/features/empatica_electrodermal_activity SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/ EMPATICA_BLOOD_VOLUME_PULSE: TABLE: BVP PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"] SRC_FOLDER: "dbdp" # inside src/features/empatica_blood_volume_pulse SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/ EMPATICA_INTER_BEAT_INTERVAL: TABLE: IBI PROVIDERS: DBDP: COMPUTE: False FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"] SRC_FOLDER: "dbdp" # inside src/features/inter_beat_interval SRC_LANGUAGE: "python" # See https://www.rapids.science/latest/features/empatica-tags/ EMPATICA_TAGS: TABLE: TAGS PROVIDERS: # None implemented yet ######################################################################################################################## # PLOTS # ######################################################################################################################## # Data quality ------ # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#1-histograms-of-phone-data-yield HISTOGRAM_PHONE_DATA_YIELD: PLOT: False # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#2-heatmaps-of-overall-data-yield HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: PLOT: False # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#3-heatmap-of-recorded-phone-sensors HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: PLOT: False # See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#4-heatmap-of-sensor-row-count 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 ------ # See https://www.rapids.science/latest/visualizations/feature-visualizations/#1-heatmap-correlation-matrix HEATMAP_FEATURE_CORRELATION_MATRIX: PLOT: False MIN_ROWS_RATIO: 0.5 CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}