Pre-processing pipeline

The pre-processing pipeline generates a processed .CSV file for data from wearable devices.

Currently, the pipeline is capable of pre-processing raw data from the following watches:

Apple Watch
Fitbit Watch
Garmin Watch
Miband
ECG data stored as a EDF file
Biovotion
Empatica

Steps to use the pipeline

The pipeline requires the pandas, numpy, pytz, datetime, os, sys, json, rowingdata, mne, and re packages.

$ git clone https://github.com/DigitalBiomarkerDiscoveryPipeline/Pre-process.git
In your local machine go to :
$ cd /Pre-process/pipeline
Run `Complete - Browse file` file or 'Complete - User path`

If using the `Complete - Browse file` version of the code

The pipeline provides an option to browse the raw file in case of Apple, Fitbit, Garmin, miband and ECG. Please make sure to use these keywords when prompted to enter the Type of watch.
For biovotion and empatica, please provide the path to the folder where all the raw files are stored.
In case of biovotion, you will be prompted to enter the Device ID, you can find the same in the file name right after the vital sign name. For instance : If the filename is bop_1566404978515_BHR_5cda2e5e70116a01001eb098_1563897600_1563903059.csv, the Device ID is 5cda2e5e70116a01001eb098
The processed .csv file will be stored in the current working directory.

If using the `Complete - User path` version of the code

Please provide the path to the file / folder for the watch selected.
For biovotion and empatica, please provide the path to the folder where all the raw files are stored.
In case of biovotion, you will be prompted to enter the Device ID, you can find the same in the file name right after the vital sign name. For instance : If the filename is bop_1566404978515_BHR_5cda2e5e70116a01001eb098_1563897600_1563903059.csv, the Device ID is 5cda2e5e70116a01001eb098
The processed .csv file will be stored in the current working directory.

If you prefer using the terminal to run the code, please follow the following steps after you clone the repository:

jupyter nbconvert --to python Complete\ -\ Browse\ file.ipynb

jupyter nbconvert --to python Complete\ -\ User\ path.ipynb

Then run the file from terminal using:

python Complete\ -\ Browse\ file.py or python Complete\ -\ User\ path.py

Functions

The pipeline currently uses the following functions.

Plugin	README
preprocessed_output	main function that calls respective functions based on choice of watch
readcsv	calls pandas read_csv function with or without header
apple	function to process raw data from apple watch
fitbit	function to process raw data from fitbit watch
garmin	function to process raw data from garmin watch
miband	function to process raw data from miband watch
ecg	function to process raw ecg data stored as a .EDF file
biovotion	function to process raw data from biovotion watch
empatica	function to process raw data from empatica watch
process_df	Modifies column names and adds watch types
output	Stores output file with watch name
get_filenames	Used by biovotion and empatica to obtain all .csv files in a folder
preprocess_empatica	Pre-processing function for empatica, used to obtain values of vitals and frequency rate
add_time_empatica	Add time function for empatica, used to add time for each vital sign

apple functions:

main : calls all other functions to process raw data
dict_df : Creates a data dictionary with information like Workout_date, Duration, Calories burnt, Mean heart rate, Maximum heart rate, and Notes
pre_process_apple : Creates a header for the apple dataframe
add_time : Calculates actual time using start time and elapsed time information
rename_cols : Renames column names of the dataframe
output_dict : Outputs apple watch processed data as .csv and data dictionary as a .json file

fitbit functions:

main : calls all other functions to process raw data
add_time : Calculates elapsed time using actual time information
rename_cols : Renames column names of the dataframe

garmin functions:

main : calls all other functions to process raw data
add_time : Obtains actual time by converting timestamp to UTC. Also calculates elapsed time using actual time information
rename_cols : Renames column names of the dataframe

miband functions:

main : calls all other functions to process raw data
add_time : Calculates elapsed time using actual time information
rename_cols : Renames column names of the dataframe

ecg functions:

get_data : Processes .EDF file to obtain raw data in a .CSV file. It stores raw data as a .CSV file in the current working directory. 
main : calls all other functions to process raw data
pre_process_ECG : Drop unnecessary data columns from raw data .CSV file
add_time : Calculates elapsed and actual time using start time and sampling rate information
rename_cols : Renames column names of the dataframe

biovotion functions:

main : calls all other functions to process raw data
extract_names : Extracts names of the vital signs being measured by the watch using .CSV filenames
read_data : Reads all CSV files in the folder containing vital sign measurements. Processes column names.
create_df_final : Creates one single dataframe
add_time : Calculates actual time by converting timestamp to UTC time. Also calculates elapsed time.

empatica functions:

read_data : Reads all .CSV files data stored in the folder and processes each file. It calculates time using sampling frequency and start time for each file.
all_dfs : All dataframes are combined to create one single dataframe
main : calls all other functions to process raw data

Continued Development

We are frequently updating this package with new devices and insights from the DBDP (Digital Biomarker Discovery Pipeline).

GeetikaSi/Pre-process