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