This repo contains the code for my publication available on MDPI.
To set up the repository, first create a virtual environment: Using uv is recommended.
uv venv --python python3.11
source .venv/bin/activate
uv pip install -r requirements.txtTo parse the sensor the package flexlib is needed. The wheels for Linux, MacOS and Windows are located in the wheels folder. Install the right version for you os with:
uv pip install wheels/flexlib-1.0.0-cp311-cp311-manylinux_2_34_x86_64.whlor
uv pip install wheels/flexlib-1.0.0-cp311-cp311-win_amd64.whlTo run the Python scripts, activate the virtual environment and execute the desired script:
python ./driver.py -w 1 -s full -m pose -c bestCheckout the script options with the help flag
python ./driver.py --helpThe jobs.txt file contains predefined commands for running experiments. Each line represents a job with specific parameters. You can execute these jobs using parallel processing:
python ./driver.py -w 1 -s combined -m flextail -c quick --inclusion_rate 0.5
python ./driver.py -w 1 -s combined -m pose -c quick --inclusion_rate 0.5
python ./driver.py -w 1 -s full -m flextail -c quick
python ./driver.py -w 1 -s full -m pose -c quickTo run all jobs in parallel:
parallel < jobs.txtThis will execute all commands in jobs.txt simultaneously, leveraging multiple CPU cores for faster processing.
- Ensure the virtual environment is activated before running any commands.
- If dependencies fail to install, verify the
requirements.txtfile and manually install missing packages.
- The repository is structured to support activity analysis and model evaluation.
- Data files are located in the
datadirectory, organized by participant and activity. - Results are stored in the
results_full_*directories for different modalities.