MUSS: Multi- vs. single-site accelerometer sensing for posture and activity recognition using machine learning

Data and reproducible code for MUSS

Under development

If you have any problem related to the data or the codes, please create a github issue.

Corresponding author

Citation

Replication of results

Under development

Multi- vs. single-site accelerometer sensing for posture and activity recognition using machine learning (MuSS)

source codes and data

Citation

In-submission.

Dependencies

  1. Python 3.6.5 or above
  2. pipenv package. Install using pip install pipenv
  3. git (optional)
  4. Recommend at least 8-core workstation, otherwise computation will be slow
  5. graphviz (optional, required to generate workflow diagram pdf)

Replication of results

>> pipenv run reproduce --help

Run above command at the root of the repository to see the usage of the reproduction script.

The reproduct script will do the following,

  1. Download and unzip the raw dataset
  2. Compute features and convert annotations to class labels
  3. Run LOSO cross validations on datasets of different combinations of sensor placements
  4. Compute metrics from the outputs of the LOSO cross validations
  5. Generate publication tables and graphs

Example

Run with multi-core processing and memory and time profiling on a new session folder. Overwrite data or results if they are found to exist.

>> pipenv run reproduce --parallel --profiling --run-ts=new --force-fresh-data=True

You may find intermediate and publication results in ./muss_data/DerivedCrossParticipants in a folder prefixed with product_run.

Sample reproduction results

Check here for a sample reproduction results.