Human Activity Detection Matlab Code

5/27/2019
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#A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors

  1. Real time human activity recognition. Learn more about image processing, human activity recognition, single camera.
  2. I have a shimmer 3D accelerator and software that write x,y,z axis data in dat file.I want to detect, real time activities detection where 128 readings/window (2.56 sec data) and 50% overlap like the dataset for human activity learning.please help me.

MATLAB toolbox for the publication

A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors
Andreas Bulling, Ulf Blanke and Bernt Schiele
ACM Computing Surveys 46, 3, Article 33 (January 2014), 33 pages
DOI: http://dx.doi.org/10.1145/2499621

If you find the toolbox useful for your research please cite the above paper, thanks!

Version 1.4, 19 August 2014

General Notes

  • The data should be arranged in a MATLAB matrix with rows denoting the frames (samples) and columnsdenoting the different sensors or axes -> matrix NxM (N: frames, M: sensors/axes)IMPORTANT: make sure the matrix does not contain any timestamp columns as often added by data recordingtoolboxes, such as the Context Recognition Network Toolbox

  • The ground truth labels should be integers, arranged in a MATLAB vector with rows denoting the frames-> vector Nx1 (N: frames)

  • The data matrix should be loaded into the variable data, the ground truth label vector intothe variable labels

  • The NULL class needs to have label 1, the remaining classes labels 2:n

  • If you want to modify the default parameters of the different classifiershave a look at setClassifier.m

  • This toolbox requires the following MATLAB toolboxes:

  • To compile the different third-party libraries have a look at the documentation Daniel defense modular float rail installation instructions.

How to reproduce the results from the paper

Execute run_experiments_paper.m in MATLAB

Specific notes on how to create and run your own experiment

Matlab

  1. Have a look at settings.mThis file contains all settings available in the toolbox and their defaults. All settings arestored in a MATLAB struct SETTINGS. Set the different fields in this structaccording to the requirements of your planned experiment.

  2. Have a look at Experiments/expTutorial.m and run the scriptThis file contains a (simple) example structure of an experiment. Note how settings.m isexecuted first, followed by modifications to the SETTINGS fields.

    optional: Install all third-party libraries you plan to use (see list below).Archives of all supported libraries are provided in the subdirectory 'Libraries'.The libraries should be installed in the same directory. If you prefer to install the librariesin a different path, adapt the library paths in settings.m accordingly (line 33 and following)

  3. To create your own experiment

  4. Copy Experiments/expTutorial.m to Experiments/expOwn.m

  5. Write code in expOwn.m to modify SETTINGS according to your experiment's requirements, in particular:

  1. Change the EXPERIMENT_NAME and IDENTIFIER_NAME variables in expOwn.mFor example, EXPERIMENT_NAME could be set to 'kNN' and IDENTIFIER_NAME to 'k_5' if yourexperiment involves using a kNN classifier with k fixed to 5.

  2. Put your data files in subdirectories of 'Data' named according to the scheme: subjectX_Y

    • X denotes the index of the subject (1:SETTINGS.SUBJECT_TOTAL)
    • Y denotes the type of dataset (SETTINGS.DATASET plus additional ones)For example, the toolbox datasets are stored in the following subdirectories:subject1_walk, subject1_gesture, subject2_walk, subject2_gestureThe data files should be called 'data.mat' and should contain both variables data and labels

  3. Run expOwn.m and wait for the script to finish.Extracted features will be saved in 'Output/features' whereas the experiment output will be savedin 'Output/experiments/EXPERIMENT_NAME/IDENTIFIER_NAME'

Flow Chart Human Activity Detection

Optional third-party libraries

  • libSVM
    URL: http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  • liblinear
    URL: http://www.csie.ntu.edu.tw/~cjlin/liblinear/

  • mRMR
    URL: http://penglab.janelia.org/proj/mRMR/

  • SVMlight
    URL: http://svmlight.joachims.org/

  • jointboosting by Christian Wojek
    URL: none

  • HMM Toolbox for MATLAB by Kevin Murphy
    URL: http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html

  • Performance evaluation scripts by Jamie Ward
    URL: http://www.jamieward.net/research/performance/

#A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors

MATLAB toolbox for the publication

A Tutorial on Human Activity Recognition Using Body-worn Inertial Sensors
Andreas Bulling, Ulf Blanke and Bernt Schiele
ACM Computing Surveys 46, 3, Article 33 (January 2014), 33 pages
DOI: http://dx.doi.org/10.1145/2499621

If you find the toolbox useful for your research please cite the above paper, thanks!

Version 1.4, 19 August 2014

Human

General Notes

  • The data should be arranged in a MATLAB matrix with rows denoting the frames (samples) and columnsdenoting the different sensors or axes -> matrix NxM (N: frames, M: sensors/axes)IMPORTANT: make sure the matrix does not contain any timestamp columns as often added by data recordingtoolboxes, such as the Context Recognition Network Toolbox

  • The ground truth labels should be integers, arranged in a MATLAB vector with rows denoting the frames-> vector Nx1 (N: frames)

  • The data matrix should be loaded into the variable data, the ground truth label vector intothe variable labels

  • The NULL class needs to have label 1, the remaining classes labels 2:n

  • If you want to modify the default parameters of the different classifiershave a look at setClassifier.m

  • This toolbox requires the following MATLAB toolboxes:

  • To compile the different third-party libraries have a look at the documentation

How to reproduce the results from the paper

Execute run_experiments_paper.m in MATLAB

Specific notes on how to create and run your own experiment

  1. Have a look at settings.mThis file contains all settings available in the toolbox and their defaults. All settings arestored in a MATLAB struct SETTINGS. Set the different fields in this structaccording to the requirements of your planned experiment.

  2. Have a look at Experiments/expTutorial.m and run the scriptThis file contains a (simple) example structure of an experiment. Note how settings.m isexecuted first, followed by modifications to the SETTINGS fields.

    optional: Install all third-party libraries you plan to use (see list below).Archives of all supported libraries are provided in the subdirectory 'Libraries'.The libraries should be installed in the same directory. If you prefer to install the librariesin a different path, adapt the library paths in settings.m accordingly (line 33 and following)

  3. To create your own experiment

  4. Copy Experiments/expTutorial.m to Experiments/expOwn.m

  5. Write code in expOwn.m to modify SETTINGS according to your experiment's requirements, in particular:

  1. Change the EXPERIMENT_NAME and IDENTIFIER_NAME variables in expOwn.mFor example, EXPERIMENT_NAME could be set to 'kNN' and IDENTIFIER_NAME to 'k_5' if yourexperiment involves using a kNN classifier with k fixed to 5.

  2. Put your data files in subdirectories of 'Data' named according to the scheme: subjectX_Y

    • X denotes the index of the subject (1:SETTINGS.SUBJECT_TOTAL)
    • Y denotes the type of dataset (SETTINGS.DATASET plus additional ones)For example, the toolbox datasets are stored in the following subdirectories:subject1_walk, subject1_gesture, subject2_walk, subject2_gestureThe data files should be called 'data.mat' and should contain both variables data and labels

  3. Run expOwn.m and wait for the script to finish.Extracted features will be saved in 'Output/features' whereas the experiment output will be savedin 'Output/experiments/EXPERIMENT_NAME/IDENTIFIER_NAME'

Optional third-party libraries

  • libSVM
    URL: http://www.csie.ntu.edu.tw/~cjlin/libsvm/

  • liblinear
    URL: http://www.csie.ntu.edu.tw/~cjlin/liblinear/

  • mRMR
    URL: http://penglab.janelia.org/proj/mRMR/

  • SVMlight
    URL: http://svmlight.joachims.org/

  • jointboosting by Christian Wojek
    URL: none

  • HMM Toolbox for MATLAB by Kevin Murphy
    URL: http://www.cs.ubc.ca/~murphyk/Software/HMM/hmm.html

  • Performance evaluation scripts by Jamie Ward
    URL: http://www.jamieward.net/research/performance/

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