Test Cases¶

Along with the continued development and the addition of new sensors and features to the RAPIDS pipeline, tests for the currently available sensors and features are being implemented. Since this is a Work In Progress this page will be updated with the list of sensors and features for which testing is available. For each of the sensors listed a description of the data used for testing (test cases) are outline. Currently for all intent and testing purposes the tests/data/raw/test01/ contains all the test data files for testing android data formats and tests/data/raw/test02/ contains all the test data files for testing iOS data formats. It follows that the expected (verified output) are contained in the tests/data/processed/test01/ and tests/data/processed/test02/ for Android and iOS respectively. tests/data/raw/test03/ and tests/data/raw/test04/ contain data files for testing empty raw data files for android and iOS respectively.

The following is a list of the sensors that testing is currently available.

Sensor	Provider	Periodic	Frequency	Event
Phone Accelerometer	Panda	Y	Y	Y
Phone Accelerometer	RAPIDS	Y	Y	Y
Phone Activity Recognition	RAPIDS	Y	Y	Y
Phone Applications Foreground	RAPIDS	Y	Y	Y
Phone Battery	RAPIDS	Y	Y	Y
Phone Bluetooth	Doryab	Y	Y	Y
Phone Bluetooth	RAPIDS	Y	Y	Y
Phone Calls	RAPIDS	Y	Y	Y
Phone Conversation	RAPIDS	Y	Y	Y
Phone Data Yield	RAPIDS	Y	Y	Y
Phone Light	RAPIDS	Y	Y	Y
Phone Locations	Doryab	Y	Y	Y
Phone Locations	Barnett	N	N	N
Phone Messages	RAPIDS	Y	Y	Y
Phone Screen	RAPIDS	Y	Y	Y
Phone WiFi Connected	RAPIDS	Y	Y	Y
Phone WiFi Visible	RAPIDS	Y	Y	Y
Fitbit Calories Intraday	RAPIDS	Y	Y	Y
Fitbit Data Yield	RAPIDS	Y	Y	Y
Fitbit Heart Rate Summary	RAPIDS	Y	Y	Y
Fitbit Heart Rate Intraday	RAPIDS	Y	Y	Y
Fitbit Sleep Summary	RAPIDS	Y	Y	Y
Fitbit Sleep Intraday	RAPIDS	Y	Y	Y
Fitbit Sleep Intraday	PRICE	Y	Y	Y
Fitbit Steps Summary	RAPIDS	Y	Y	Y
Fitbit Steps Intraday	RAPIDS	Y	Y	Y

Accelerometer¶

Description

The raw accelerometer data file, phone_accelerometer_raw.csv, contains data for 4 separate days
One episode for each daily segment (night, morning, afternoon and evening)
Two episodes locate in the same 30-min segment (Fri 00:15:00 and Fri 00:21:21)
Two episodes locate in the same daily segment (Fri 00:15:00 and Fri 18:12:00)
One episode before the time switch (Sun 00:02:00) and one episode after the time switch (Sun 04:18:00)
Multiple episodes within one min which cause variance in magnitude (Fri 00:10:25, Fri 00:10:27 and Fri 00:10:46)

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android, ios
morning	OK	OK	android, ios
daily	OK	OK	android, ios
threeday	OK	OK	android, ios
weekend	OK	OK	android, ios
beforeMarchEvent	OK	OK	android, ios
beforeNovemberEvent	OK	OK	android, ios

Messages (SMS)¶

Description

The raw message data file, phone_messages_raw.csv, contains data for 4 separate days
One episode for each daily segment (night, morning, afternoon and evening)
Two sent episodes locate in the same 30-min segment (Fri 16:08:03.000 and Fri 16:19:35.000)
Two received episodes locate in the same 30-min segment (Sat 06:45:05.000 and Fri 06:45:05.000)
Two episodes locate in the same daily segment (Fri 11:57:56.385 and Sat 10:54:10.000)
One episode before the time switch (Sun 00:48:01.000) and one episode after the time switch (Sun 06:21:01.000)

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android
morning	OK	OK	android
daily	OK	OK	android
threeday	OK	OK	android
weekend	OK	OK	android
beforeMarchEvent	OK	OK	android
beforeNovemberEvent	OK	OK	android

Calls¶

Due to the difference in the format of the raw data for iOS and Android the following is the expected results the phone_calls.csv.

Description

One missed episode, one outgoing episode and one incoming episode on Friday night, morning, afternoon and evening
There is at least one episode of each type of phone calls on each day
One incoming episode crossing two 30-mins segments
One outgoing episode crossing two 30-mins segments
One missed episode before, during and after the event
There is one incoming episode before, during or after the event
There is one outcoming episode before, during or after the event
There is one missed episode before, during or after the event

Data format

Device	Missed	Outgoing	Incoming
android	3	2	1
ios	1,4 or 3,4	3,2,4	1,2,4

Note When generating test data, all traces for iOS device need to be unique otherwise the episode with duplicate trace will be dropped

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android, iOS
morning	OK	OK	android, iOS
daily	OK	OK	android, iOS
threeday	OK	OK	android, iOS
weekend	OK	OK	android, iOS
beforeMarchEvent	OK	OK	android, iOS
beforeNovemberEvent	OK	OK	android, iOS

Screen¶

Due to the difference in the format of the raw screen data for iOS and Android the following is the expected results the phone_screen.csv.

Description

The screen data file contains data for 4 days.
The screen data contains 1 record to represent an unlock episode that falls within an epoch for every epoch.
The screen data contains 1 record to represent an unlock episode that falls across the boundary of 2 epochs. Namely the unlock episode starts in one epoch and ends in the next, thus there is a record for unlock episodes that fall across night to morning, morning to afternoon and finally afternoon to night
One episode that crossing two 30-min segments

Data format

Device	unlock
Android	3, 0
iOS	3, 2

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android, iOS
morning	OK	OK	android, iOS
daily	OK	OK	android, iOS
threeday	OK	OK	android, iOS
weekend	OK	OK	android, iOS
beforeMarchEvent	OK	OK	android, iOS
beforeNovemberEvent	OK	OK	android, iOS

Battery¶

Description

The 4-day raw data is contained in phone_battery_raw.csv
One discharge episode acrossing two 30-min time segements (Fri 05:57:30.123 to Fri 06:04:32.456)
One charging episode acrossing two 30-min time segments (Fri 11:55:58.416 to Fri 12:08:07.876)
One discharge episode and one charging episode locate within the same 30-min time segement (Fri 21:30:00 to Fri 22:00:00)
One episode before the time switch (Sun 00:24:00.000) and one episode after the time switch (Sun 21:58:00)
Two episodes locate in the same daily segment

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android
morning	OK	OK	android
daily	OK	OK	android
threeday	OK	OK	android
weekend	OK	OK	android
beforeMarchEvent	OK	OK	android
beforeNovemberEvent	OK	OK	android

Bluetooth¶

Description

The 4-day raw data is contained in phone_bluetooth_raw.csv
One episode for each daily segment (night, morning, afternoon and evening)
Two episodes locate in the same 30-min segment (Fri 23:38:45.789 and Fri 23:59:59.465)
Two episodes locate in the same daily segment (Fri 00:00:00.798 and Fri 00:49:04.132)
One episode before the time switch (Sun 00:24:00.000) and one episode after the time switch (Sun 17:32:00.000)

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android
morning	OK	OK	android
daily	OK	OK	android
threeday	OK	OK	android
weekend	OK	OK	android
beforeMarchEvent	OK	OK	android
beforeNovemberEvent	OK	OK	android

WIFI¶

There are two wifi features (phone wifi connected and phone wifi visible). The raw test data are seperatly stored in the phone_wifi_connected_raw.csv and phone_wifi_visible_raw.csv.

Description

One episode for each epoch (night, morining, afternoon and evening)
Two two episodes in the same time segment (daily and 30-min)
Two episodes around the transition of epochs (e.g. one at the end of night and one at the beginning of morning)
One episode before and after the time switch on Sunday

phone wifi connected

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android, iOS
morning	OK	OK	android, iOS
daily	OK	OK	android, iOS
threeday	OK	OK	android, iOS
weekend	OK	OK	android, iOS
beforeMarchEvent	OK	OK	android, iOS
beforeNovemberEvent	OK	OK	android, iOS

phone wifi visible

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android
morning	OK	OK	android
daily	OK	OK	android
threeday	OK	OK	android
weekend	OK	OK	android
beforeMarchEvent	OK	OK	android
beforeNovemberEvent	OK	OK	android

Light¶

Description

The 4-day raw light data is contained in phone_light_raw.csv
One episode for each daily segment (night, morning, afternoon and evening)
Two episodes locate in the same 30-min segment (Fri 00:07:27.000 and Fri 00:12:00.000)
Two episodes locate in the same daily segment (Fri 01:00:00 and Fri 03:59:59.654)
One episode before the time switch (Sun 00:08:00.000) and one episode after the time switch (Sun 05:36:00.000)

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android
morning	OK	OK	android
daily	OK	OK	android
threeday	OK	OK	android
weekend	OK	OK	android
beforeMarchEvent	OK	OK	android
beforeNovemberEvent	OK	OK	android

Locations¶

Description

The participant’s home location is (latitude=1, longitude=1).
From Sat 10:56:00 to Sat 11:04:00, the center of the cluster is (latitude=-100, longitude=-100).
From Sun 03:30:00 to Sun 03:47:00, the center of the cluster is (latitude=1, longitude=1). Home location is extracted from this period.
From Sun 11:30:00 to Sun 11:38:00, the center of the cluster is (latitude=100, longitude=100).

Application Foreground¶

The 4-day raw application data is contained in phone_applications_foreground_raw.csv
One episode for each daily segment (night, morning, afternoon and evening)
Two episodes locate in the same 30-min segment (Fri 10:12:56.385 and Fri 10:18:48.895)
Two episodes locate in the same daily segment (Fri 11:57:56.385 and Fri 12:02:56.385)
One episode before the time switch (Sun 00:07:48.001) and one episode after the time switch (Sun 05:10:30.001)
Two custom category (Dating) episode, one at Fri 06:05:10.385, another one at Fri 11:53:00.385

Checklist:

time segment	single tz	multi tz	platform
30min	OK	OK	android
morning	OK	OK	android
daily	OK	OK	android
threeday	OK	OK	android
weekend	OK	OK	android
beforeMarchEvent	OK	OK	android
beforeNovemberEvent	OK	OK	android

Activity Recognition¶

Description

The 4-day raw activity data is contained in plugin_google_activity_recognition_raw.csv and plugin_ios_activity_recognition_raw.csv.
Two episodes locate in the same 30-min segment (Fri 04:01:54 and Fri 04:13:52)
One episode for each daily segment (night, morning, afternoon and evening)
Two episodes locate in the same daily segment (Fri 05:03:09 and Fri 05:50:36)
Two episodes with the time difference less than 5 mins threshold (Fri 07:14:21 and Fri 07:18:50)
One episode before the time switch (Sun 00:46:00) and one episode after the time switch (Sun 03:42:00)

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android, iOS
morning	OK	OK	android, iOS
daily	OK	OK	android, iOS
threeday	OK	OK	android, iOS
weekend	OK	OK	android, iOS
beforeMarchEvent	OK	OK	android, iOS
beforeNovemberEvent	OK	OK	android, iOS

Conversation¶

The 4-day raw conversation data is contained in phone_conversation_raw.csv. The different inference records are randomly distributed throughout the epoch.

Description

One episode for each daily segment (night, morning, afternoon and evening) on each day
Two episodes near the transition of the daily segment, one starts at the end of the afternoon, Fri 17:10:00 and another one starts at the beginning of the evening, Fri 18:01:00
One episode across two segments, daily and 30-mins, (from Fri 05:55:00 to Fri 06:00:41)
Two episodes locate in the same daily segment (Sat 12:45:36 and Sat 16:48:22)
One episode before the time switch, Sun 00:15:06, and one episode after the time switch, Sun 06:01:00

Data format

inference	type
0	silence
1	noise
2	voice
3	unknown

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android
morning	OK	OK	android
daily	OK	OK	android
threeday	OK	OK	android
weekend	OK	OK	android
beforeMarchEvent	OK	OK	android
beforeNovemberEvent	OK	OK	android

Keyboard¶

The raw keyboard data file contains data for 4 days.
The raw keyboard data contains records with difference in timestamp ranging from milliseconds to seconds.
With difference in timestamps between consecutive records more than 5 seconds helps us to create separate sessions within the usage of the same app. This helps to verify the case where sessions have to be different.
The raw keyboard data contains records where the difference in text is less than 5 seconds which makes it into 1 session but because of difference of app new session starts. This edge case determines the behaviour within particular app and also within 5 seconds.
The raw keyboard data also contains the records where length of current_text varies between consecutive rows. This helps us to tests on the cases where input text is entered by auto-suggested or auto-correct operations.
One three-minute episode with a 1-minute row on Sun 08:59:54.65 and 09:00:00,another on Sun 12:01:02 that are considering a single episode in multi-timezone event segments to showcase how inferring time zone data for Keyboard from phone data can produce inaccurate results around the tz change. This happens because the device was on LA time until 11:59 and switched to NY time at 12pm, in terms of actual time 09 am LA and 12 pm NY represent the same moment in time so 09:00 LA and 12:01 NY are consecutive minutes.

Application Episodes¶

The feature requires raw application foreground data file and raw phone screen data file
The raw data files contains data for 4 day.
The raw conversation data contains records with difference in timestamp ranging from milliseconds to minutes.
An app episode starts when an app is launched and ends when another app is launched, marking the episode end of the first one, or when the screen locks. Thus, we are taking into account the screen unlock episodes.
There are multiple apps usage within each screen unlock episode to verify creation of different app episodes in each screen unlock session. In the screen unlock episode starting from Fri 05:56:51, Fri 10:00:24, Sat 17:48:01, Sun 22:02:00, and Mon 21:05:00 we have multiple apps, both system and non-system apps, to check this.
The 22 minute chunk starting from Fri 10:03:56 checks app episodes for system apps only.
The screen unlock episode starting from Mon 21:05:00 and Sat 17:48:01 checks if the screen lock marks the end of episode for that particular app which was launched a few milliseconds to 8 mins before the screen lock.
Finally, since application foreground is only for Android devices, this feature is also for Android devices only. All other files are empty data files

Data Yield¶

Description

Two sensors were picked for testing, phone_screen and phone_light. phone_screen is event based and phone_light is sampling at regular frequency
A 31-min episode (from Fri 01:00:00 to Fri 01:30:00) in phone_light data, which is considered as a validyieldedhours

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	android, ios
morning	OK	OK	android, ios
daily	OK	OK	android, ios
threeday	OK	OK	android, ios
weekend	OK	OK	android, ios
beforeMarchEvent	OK	OK	android, ios
beforeNovemberEvent	OK	OK	android, ios

Fitbit Calories Intraday¶

Description

A five-minute sedentary episode on Fri 11:00:00
A one-minute sedentary episode on Sun 02:00:00. It exists in November but not in February in STZ
A five-minute sedentary episode on Fri 11:58:00. It is split within two 30-min segments and the morning
A three-minute lightly active episode on Fri 11:10:00, a one-minute at 11:18:00 and a one-minute 11:24:00. These check for start and end times of first/last/longest episode
A three-minute fairly active episode on Fri 11:40:00, a one-minute at 11:48:00 and a one-minute 11:54:00. These check for start and end times of first/last/longest episode
A three-minute very active episode on Fri 12:10:00, a one-minute at 12:18:00 and a one-minute 12:24:00. These check for start and end times of first/last/longest episode
A eight-minute MVPA episode with intertwined fairly and very active rows on Fri 12:30:00
The above episodes contain six higmet (>= 3 MET) episodes and nine lowmet episodes.
One two-minute sedentary episode with a 1-minute row on Sun 09:00:00 and another on Sun 12:01:01 that are considering a single episode in multi-timezone event segments to showcase how inferring time zone data for Fitbit from phone data can produce inaccurate results around the tz change. This happens because the device was on LA time until 11:59 and switched to NY time at 12pm, in terms of actual time 09 am LA and 12 pm NY represent the same moment in time so 09:00 LA and 12:01 NY are consecutive minutes.
A three-minute sedentary episode on Sat 08:59 that will be ignored for multi-timezone event segments.
A three-minute sedentary episode on Sat 12:59 of which the first minute will be ignored for multi-timezone event segments since the test segment starts at 13:00
A three-minute sedentary episode on Sat 16:00
A four-minute sedentary episode on Sun 10:01 that will be ignored for Novembers’s multi-timezone event segments since the test segment ends at 10am on that weekend.
A three-minute very active episode on Sat 16:03. This episode and the one at 16:00 are counted as one for lowmet episodes

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Heartrate intraday¶

Description:

The 4-day raw heartrate data is contained in fitbit_heartrate_intraday_raw.csv
One episode for each daily segment (night, morning, afternoon and evening)
Two episodes locate in the same 30-min segment (Fri 00:49:00 and Fri 00:52:00)
Two different types of heartrate zone episodes locate in the same 30-min segment (Fri 05:49:00 outofrange and Fri 05:57:00 fatburn)
Two episodes locate in the same daily segment (Fri 12:02:00 and Fri 19:38:00)
One episode before the time switch, Sun 00:08:00, and one episode after the time switch, Sun 07:28:00

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Sleep Summary¶

Description

A main sleep episode that starts on Fri 20:00:00 and ends on Sat 02:00:00. This episode starts after 11am (Last Night End) which will be considered as today’s (Fri) data.
A nap that starts on Sat 04:00:00 and ends on Sat 06:00:00. This episode starts before 11am (Last Night End) which will be considered as yesterday’s (Fri) data.
A nap that starts on Sat 13:00:00 and ends on Sat 15:00:00. This episode starts after 11am (Last Night End) which will be considered as today’s (Sat) data.
A main sleep that starts on Sun 01:00:00 and ends on Sun 12:00:00. This episode starts before 11am (Last Night End) which will be considered as yesterday’s (Sat) data.
A main sleep that starts on Sun 23:00:00 and ends on Mon 07:00:00. This episode starts after 11am (Last Night End) which will be considered as today’s (Sun) data.
Any segment shorter than one day will be ignored for sleep RAPIDS features.

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Sleep Intraday¶

Description

A five-minute main sleep episode with asleep-classic level on Fri 11:00:00.
An eight-hour main sleep episode on Fri 17:00:00. It is split into 2 parts for daily segment: a seven-hour sleep episode on Fri 17:00:00 and an one-hour sleep episode on Sat 00:00:00.
A two-hour nap on Sat 01:00:00 that will be ignored for main sleep features.
An one-hour nap on Sat 13:00:00 that will be ignored for main sleep features.
An eight-hour main sleep episode on Sat 22:00:00. This episode ends on Sun 08:00:00 (NY) for March and Sun 06:00:00 (NY) for Novembers due to daylight savings. It will be considered for beforeMarchEvent segment and ignored for beforeNovemberEvent segment.
A nine-hour main sleep episode on Sun 11:00:00. Start time will be assigned as NY time zone and converted to 14:00:00.
A seven-hour main sleep episode on Mon 06:00:00. This episode will be split into two parts: a five-hour sleep episode on Mon 06:00:00 and a two-hour sleep episode on Mon 11:00:00. The first part will be discarded as it is before 11am (Last Night End)
Any segment shorter than one day will be ignored for sleep PRICE features.

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Heartrate Summary¶

Description

The 4-day raw heartrate summary data is contained in fitbit_heartrate_summary_raw.csv.
As heartrate summary is periodic, it only generates results in periodic feature, there will be no result in frequency and event.

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Step Intraday¶

Description

The 4-day raw heartrate summary data is contained in fitbit_steps_intraday_raw.csv
One episode for each daily segment (night, morning, afternoon and evening) on each day
Two episodes within the same 30-min segment (Fri 05:58:00 and Fri 05:59:00)
A one-min episode at 2020-03-07 09:00:00 that will be converted to New York time 2020-03-07 12:00:00
One episode before the time switch, Sun 00:19:00, and one episode after the time switch, Sun 09:01:00
Episodes cross two 30-min segments (Fri 11:59:00 and Fri 12:00:00)

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Step Summary¶

Description

The 4-day raw heartrate summary data is contained in fitbit_steps_summary_raw.csv.
As heartrate summary is periodic, it only generates results in periodic feature, there will be no result in frequency and event.

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit

Fitbit Data Yield¶

Checklist

time segment	single tz	multi tz	platform
30min	OK	OK	fitbit
morning	OK	OK	fitbit
daily	OK	OK	fitbit
threeday	OK	OK	fitbit
weekend	OK	OK	fitbit
beforeMarchEvent	OK	OK	fitbit
beforeNovemberEvent	OK	OK	fitbit