Educational Activity Recognition Framework and Dataset
The framework is implemented in MATLAB and has the below features. The framework is accompanied with an educational dataset as well as a number of example scripts that produce all the results described in the paper. The framework and dataset can be downloaded HERE.
General
- Support for arbitrary number and type of sensors with different configurations and placements, participants, as well as activity classes
- Fully parametrized and customizable (window size, step size, number of features, etc)
- Different evaluation methods (frame-based, activity event-based)
- Plotting functions (confusion matrix, feature ranking, feature distribu- tions, PR- and ROC-curves, parameter sweeps)
- Easily extendible with new classifiers, features, etc.
- Large number of sample evaluations (all of those described in the paper)
- Experiments-Pipeline: Effortless adding of new experimental settings
Classifiers
- Discriminant Analysis (Matlab)
- Discrete/continuous Hidden Markov models (HMMToolbox, Kevin Murphy)
- Support vector machine (SVM light/liblinear, C++)
- JointBoosting (Torralba, Implementation Wojek C++)
- k-nearest neighbour (kNN, Matlab)
- Nearest class centre (NCC, Matlab)
- Naive Bayes (Matlab)
Segmentation methods
- Sliding Window
- Energy-based
Supported features
- Mean
- Variance
- MCR (mean crossing rate)
- ZCR (zero crossing rate)
- Several FFT features
Feature selection methods
- Minimum redundancy maximum relevance (mRMR, filter)
- Sequential forward selection (SFS, wrapper)
- Sequential backward selection (SBS, wrapper)
Decision fusion approaches
- Early (feature-level)
- Late (classifier level)
Evaluations
- Person-independent
- Person-dependent
- Cross validation leave-one-person,fold-out
- Evaluation tools:
- Confusion matrix
- Timeframe-based evaluation
- Precision-recall curves + AUC, equal error rate (EER), average precision (AVGPREC)
- Event based criterion (hit and min50)
- Output label statistics