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Rob MacKinnon 5e6bb15d1c Code review and PEP8 clean-ups 
- PEP8 formating clean-up
- bluesleep.py: Applied re-use of dict objects to variables for
     readability/PEP8.
- bluesleep.py: Updated for loops on items method to keys method as
     value is unused.
- bluesleep.py::get_auth_key: Refactor for readability/PEP8.
- bluesleep.py::average_raw_data: Simplified code and str formatting.
- bluesleep.py::sleep_monitor_callback: Pulled `data[0]` tests to
     if-elif.
- bluesleep.py::connect: Removal of `break` and `continue`, 
     `initialize()` returning true will exit the loop.  No need to break
     or continue the loop from within a try-except.
- bluesleep.py::connect: Removal of magic value on.
- bluesleep.py::graph_animation: Updated axis formating
- miband.py: Reformat imports
 		2021-01-25 08:56:06 -08:00
.gitignore Added timestamps to CSV logging 2021-01-24 13:04:28 -08:00
README Resetting 2021-01-24 02:45:37 -08:00
bluesleep.py Code review and PEP8 clean-ups 2021-01-25 08:56:06 -08:00
constants.py Resetting 2021-01-24 02:45:37 -08:00
miband.py Code review and PEP8 clean-ups 2021-01-25 08:56:06 -08:00
requirements.txt Resetting 2021-01-24 02:45:37 -08:00

README 

BLESleep: A project to implement sleep tracking and nightmare interruption on Linux using the Mi Band 4 fitness tracker
It is inspired by the "Nightware" device, which is an FDA approved medical device: https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-new-device-designed-reduce-sleep-disturbance-related-nightmares-certain-adults

This project is based on the satcar77/miband4 project.

USAGE: 
You will need to create two .txt files in the base directory:
  auth_key.txt: the authentication key for your miband 4.  See https://github.com/argrento/huami-token for details on obtaining this.
  mac.txt: the Bluetooth MAC address for your miband 4.


CURRENT STATUS:
Right now the project does not yet fulfill its purpose.  
If you execute the project as-is, it will connect to the miband and plot HR and gyroscope data to the screen.
I'm currently working on massaging the returned data to create signals that can be usefully incorporated into a detection algorithm.

GOALS:
* Publish statistics to Google Sheets or other easy-to-use target.  I'd like to make this usable by non-technical folks, so S3 buckets and CloudWatch are out of scope.
* Incorporate an LSTM neural network as the detection algorithm.  This will require a dedicated "training period", as each user will likely produce different signals.
* Create a GUI dashboard to display sleep statistics and other controls.  My vision for the end product is a standalone RasPi device with a 7" touchscreen display.
* Add support for multiple (cheap) fitness trackers.  I'd like this work to be usable by low-income folks who can't afford Apple Watches.
* Create a framework to incorporate additional biometrics/signals as they become available.  For example, SpO2, galvanic skin response, respiration, BP, etc.
* Create a holistic sleep-tracking interface to provide users the ability to clearly observe the effects of things like changes in routine, medication, diet, etc.


DISCLAIMER:
None of the statements on this web site have been evaluated by the FDA.
Furthermore, none of the statements herein should be construed as dispensing medical advice, or making claims regarding the cure or treatment of diseases. 
These statements have not been evaluated by the Food and Drug Administration. 
These project is not intended to diagnose, treat, cure, or prevent any diseases.