Sparse Representation/Reconstruction of Plantar Pressure Image (MLHC2017)
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Dataset
Dictionaries
Images
Mask
KSVD.m
KSVD_NN.m
NN_BP.m
OMP.m
OMPerr.m
ORMP.m
README.md
RFOCUSS.m
analyzeAll.m
fpreconst.m
fpreconst_gmm.m
fpreconst_interp.m
gmmError.m
learndic.m
learningtest.m
my_im2col.m
my_plot.m
my_plot_all.m
pickKsensor.m
testallfunc.m
textprogressbar.m

README.md

Plantar Pressure Reconstruction Using Compressive Sensing

This is the code for the following paper:

Farnoosh, A., Ostadabbas, S., & Nourani, M. (2017, November). Spatially-Continuous Plantar Pressure Reconstruction Using Compressive Sensing. In Machine Learning for Healthcare Conference (pp. 13-24).

Algorithm framewrk

Contact: Amirreza Farnoosh, Sarah Ostadabbas

Contents

1. Requirement

2. Dataset

The dataset comes from the original work of A knowledge-based modeling for plantar pressure image reconstruction and is placed in ./Dataset/ directory. It includes plantar pressure readings from 5 healthy subjects (right/left foot), and is augmented with the corresponding GMM centroids and variances from their method, as well as other information/preprocessing needed for our method.

3. Training and Evaluation

  • Run learningtest.m and set dict.learn flag to learn dictionary, and get evaluation plots for each specified subject and foot. You may set dict.learn = 0 if you wish to use pretrained dictionaries in ./Dictionaries/ directory to get evaluation plots.

  • Run analyzeAll.m to get aggregated evaluation results for all subjects and feet. Again set dict.learn = 0 if you want to use the pretrained dictionaries.

  • Uncomment the corresponding lines (40-43) in testallfunc.m if you want to switch between sparse reconstruction (fpreconst), interpolation (fpreconst_interp), and GMM method (fpreconst_gmm).

  • Edit line 32 of testallfunc.m to specify the set of number of sensors (K) for which you want to get evaluation results. By default K is from 4 sensors (atleast) to 46.

  • Set the corresponding figure flags to get the desired graphs.

Citation

If you find our work useful in your research please consider citing our paper:

@inproceedings{farnoosh2017spatially,
  title={Spatially-Continuous Plantar Pressure Reconstruction Using Compressive Sensing},
  author={Farnoosh, Amirreza and Ostadabbas, Sarah and Nourani, Mehrdad},
  booktitle={Machine Learning for Healthcare Conference},
  pages={13--24},
  year={2017}
}

License

  • This code is for non-commercial purpose only. For other uses please contact ACLab of NEU.
  • No maintenance service