Muvr

muvr is a demonstration of an application that uses wearable devices (Pebble)—in combination with a mobile app—to submit physical (i.e. accelerometer, compass) and biological (i.e. heart rate) information to a CQRS/ES cluster to be analysed.

muvr-ml

muvr-analytics contains machine learning pipelines external to the main application, including

• pipelines to suggest future exercise sessions
• pipelines to classify users to groups by attribute similarity
• pipelines to improve classification and other models
• pipelines to train models to recognize repetitions of exercises

This part of the project can be viewed as a data science playground. The models used in the application are trained using python and exports the parameters of the trained model (configuration and weights).

Other components of the system

• muvr-server CQRS/ES cluster
• muvr-ios iOS application showcasing mobile machine learning and data collection
• muvr-pebble Pebble application, example implementation of a wearable device
• muvr-preclassification mobile data processing and classification

Getting started

Basic information to get started is below. Please also have a look at the other components of the system to get a better understanding how everything fits together.

Clone

git clone [email protected]:muvr/muvr-ml.git

There are two ways to get started and work with the code, either by using docker (recommended) or by manually creating conda environment and install the dependenceis.

Using Docker [recommended]

All of the following commands assumes you have the data on the parent directory as this: ../muvr-exercise-data. But you can override that data path by passing DATA arg to the command e.g.: make dev DATA=~/my-data/my-exercises

Build the container and start a shell

$ make dev
# data will be mounted in /data
# code will be mounted in /src

Start Jupyter notebook

$ make notebook

Run tests

$ make test

Manual setup on Mac OS

Install miniconda for Mac:

http://conda.pydata.org/docs/download.html

Build and Source the Environment

# build the environment
$ ./venv.sh

# source the environment
$ source activate muvr_ml

# link the data directory. Make sure /data doesn't exist before the following step
$ sudo ln -s "${HOME}/Google Drive/Exercise Data" /data
$ sudo chown `whoami` /data

Start jupyter notebook

$ jupyter notebook

Run tests

nosetests -v */*_test.py

Issues

For any bugs or feature requests please:

1. Search the open and closed issues list to see if we're already working on what you have uncovered.
2. Make sure the issue / feature gets filed in the relevant components (e.g. server, analytics, ios)
3. File a new issue or contribute a pull request

License

Please have a look at the LICENSE file.