microt-preprocessing 0.1.19

A package that transforms raw sensor data collected from Time study app into intermediate CSV file for analysis of various purposes

Introduction

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What is this repository for?

• This repository include Python scripts that transform raw sensor data collected from Android mobile devices into intermediate CSV file for analysis of various purposes.
• The preprocessing scripts here intend to be used to provide reliable, consistent, and structured intermediate data file for data analysis related to MicroT project.

Summary of Preprocessed Intermediate File

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The discussion on data source to include can bee found in the #86 Issue of DataProcessing repository

1. Android Smartwatch

Information	Raw Data File	Included Data	Omitted Data	Intermediate file
Prompt response	[participant_folder] / logs-watch / DATE / HOUR / PromptResponses.log.csv	ID, EMA_type, Date, Prompt Timestamp, Time Zone, Completion Status, Reprompt, Response Timestamp, Q-Key, Response	None	[save_path]/[participant_id]/DATE/watch_prompt_response_PARTICIPANT_DATE.csv
Battery level	[participant_folder] / data-watch / DATE / HOUR / Battery.##.event.csv	timestamp, battery_level, battery_charging	None	[save_path]/[participant_id]/DATE/watch_battery_PARTICIPANT_DATE.csv
Accelerometer data	[participant_folder] / data-watch / DATE / HOUR / AndroidWearWatch-AccelerationCalibrated-NA.*.sensor.baf	header_timestamp, accelation_meters_per_second_squared(X,Y,Z axis), MIMS-unit	None	[participant_folder] / data-watch / DATE / HOUR / 020000000000-AccelerationCalibrated.*.sensor.csv, mims_DATE_HOUR.csv; a copy of former two in [save_path]/[participant_id]/DATE/
App usages	[participant_folder] / data-watch / DATE / HOUR / AppEventCounts.csv	log_time, last_hour_timestamp, current_hour_time_stamp, app_package_name, event_time_stamp, app_event	None	[save_path]/[participant_id]/DATE/phone_app_usage_PARTICIPANT_DATE.csv

2. Android Smartphone

Information	Raw Data File	Included Data	Omitted Data	Intermediate file
Prompt response	[participant_folder] / logs / DATE / HOUR / PromptResponses.log.csv	ID, EMA_type, Date, Prompt Timestamp, Time Zone, Completion Status, Reprompt, Response Timestamp, Q-Key, Response	None	[save_path]/[participant_id]/DATE/phone_prompt_response_PARTICIPANT_DATE.csv
GPS data	[participant_folder] / data / DATE / HOUR / GPS.csv	log_time, location_time, lat, long, horizontal_accuracy, provider, speed, altitude, bearing	None	[save_path]/[participant_id]/DATE/phone_GPS_PARTICIPANT_DATE.csv
step count	[participant_folder] / data / DATE / HOUR / StepCounterService.csv	log_time_stamp, steps_last_hour, accumulated_steps	None	[save_path]/[participant_id]/DATE/phone_stepCount_PARTICIPANT_DATE.csv
Phone state and detected activities	[participant_folder] / data / DATE / HOUR / ActivityDetected.csv	log_time, in_vehicle, on_bike, on_foot, running, still, tilting, walking, unknown	None	[save_path]/[participant_id]/DATE/phone_detected_activity_PARTICIPANT_DATE.csv
Phone usage events and broadcasts				[save_path]/[participant_id]/DATE/phone_usage_broadcasts_PARTICIPANT_DATE.csv
Environmental sensors	LightSensorStats.csv, ProximitySensorManagerService.csv, AmbientPressManagerService.csv, AmbientTempManagerService.csv, AmbientHumidManagerService.csv	log time, sensor value, sensor max	None

Code Book

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Detailed explanation of columns in intermediate files can be found in the code book.

Python Version

Python 3.6+ (Other versions haven't been tested but should be fine)

Dependencies

• For user who wants to include accelerometer and MIMS-unit, the below are extra set-up:
  1. The MIMS-unit depends on particular R package, so install R on your system.
  2. Add R to your environment variables. For windowsOS users, put path similar to "C:\Program Files\R\R-4.0.2\bin\x64" to your path in system variables, and reboot your computer.

Usage Option1: Install and use as a python package

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Install package

The pypi link can be accessed here.

> pip install microt-preprocessing

Import package in python

from microt_preprocessing import time_study_preprocessing_main

microT_root_path = <microT_root_path>                                        # path to the data source folder
intermediate_file_save_path = <intermediate_file_save_path>                  # path to the destination folder
decrypt_password = <decrypt_password>                                        # decryption password for GPS file
delete_raw = "0" or "1"				                                         # "0" denotes not deleting data source, "1" denotes deleting data source
date = "2020-06-11"			                                                 # specific dates to be preprocessed

time_study_preprocessing_main.preprocessing_all_ema.run_ema_main(microT_root_path, intermediate_file_save_path, decrypt_password, delete_raw, date)

Usage Option2: Clone this project and run scripts

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• Run script

  • For smartphone

   python preprocessing_all_ema.py <microT_root_path> <intermediate_file_save_path> [date_start]
  

  • For smartwatch

   python preprocessing_all_uema.py <microT_root_path> <intermediate_file_save_path> <participants_included_text_file_path> [date_start] [date_end]
  

Who do I talk to?

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Maintained by Aditya and Jixin