java/src/org/wiigee/logic/GestureModel.java

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+/*
+ * wiigee - accelerometerbased gesture recognition
+ * Copyright (C) 2007, 2008, 2009 Benjamin Poppinga
+ * 
+ * Developed at University of Oldenburg
+ * Contact: wiigee@benjaminpoppinga.de
+ *
+ * This file is part of wiigee.
+ *
+ * wiigee is free software; you can redistribute it and/or modify
+ * it under the terms of the GNU Lesser General Public License as published by
+ * the Free Software Foundation; either version 2 of the License, or
+ * (at your option) any later version.
+ *
+ * This program is distributed in the hope that it will be useful,
+ * but WITHOUT ANY WARRANTY; without even the implied warranty of
+ * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ * GNU Lesser General Public License for more details.
+ * 
+ * You should have received a copy of the GNU Lesser General Public License along
+ * with this program; if not, write to the Free Software Foundation, Inc.,
+ * 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
+ */
+
+package org.wiigee.logic;
+
+import java.util.Vector;
+import org.wiigee.event.AccelerationEvent;
+import org.wiigee.util.Log;
+
+/** 
+ * This Class units a Quantizer-Component and an Model-Component.
+ * In this implementation a k-mean-algorithm for quantization and
+ * a hidden markov model as instance for the model has been used.
+ * 
+ * @author Benjamin 'BePo' Poppinga
+ */
+public class GestureModel {
+	
+	/** The number of states the hidden markov model consists of */
+	private int numStates;
+	
+	/** The number of observations for the hmm and k-mean */
+	private int numObservations;
+	
+	/** The quantization component */
+	private Quantizer quantizer;
+	
+	/** The statistical model, hidden markov model */
+	private HMM markovmodell;
+
+	/** The default probability of this gesturemodel,
+	 * needed for the bayes classifier */
+	private double defaultprobability;
+
+	
+	/** Creates a Unit (Quantizer&Model).
+	 * 
+	 * @param id
+	 *  int representation of a gesture "name"/class.
+	 */
+	public GestureModel() {
+		this.numStates=8; // n=8 states empirical value
+		this.numObservations=14; // k=14 observations empirical value
+		this.markovmodell = new HMM(numStates, numObservations); // init model
+		this.quantizer = new Quantizer(numStates); // init quantizer
+	}
+
+	/**
+	 * Trains the model to a set of motion-sequences, representing
+	 * different evaluations of a gesture
+	 * 
+	 * @param trainsequence	a vector of gestures
+	 */
+	public void train(Vector<Gesture> trainsequence) {
+		// summarize all vectors from the different gestures in one
+		// gesture called sum.
+		double maxacc=0;
+		double minacc=0;
+		Gesture sum = new Gesture();
+		
+		for(int i=0; i<trainsequence.size(); i++) {
+			Vector<AccelerationEvent> t = trainsequence.elementAt(i).getData();
+
+			// add the max and min acceleration, we later get the average
+			maxacc+=trainsequence.elementAt(i).getMaxAcceleration();
+			minacc+=trainsequence.elementAt(i).getMinAcceleration();
+			
+			// transfer every single accelerationevent of each gesture to
+			// the new gesture sum
+			for(int j=0; j<trainsequence.elementAt(i).getData().size(); j++) {
+				sum.add(t.elementAt(j));
+			}
+			
+		}
+		
+		// get the average and set it to the sum gesture
+		sum.setMaxAndMinAcceleration(maxacc/trainsequence.size(), minacc/trainsequence.size());
+		
+		// train the centeroids of the quantizer with this master gesture sum.
+		this.quantizer.trainCenteroids(sum);
+		
+		// convert gesture vector to a sequence of discrete values
+		Vector<int[]> seqs = new Vector<int[]>();
+		for(int i=0; i<trainsequence.size(); i++) {
+			seqs.add(this.quantizer.getObservationSequence(trainsequence.elementAt(i)));
+		}
+		
+		// train the markov model with this derived discrete sequences
+		this.markovmodell.train(seqs);
+		
+		// set the default probability for use with the bayes classifier
+		this.setDefaultProbability(trainsequence);
+	}
+
+	/** 
+	 * Returns the probability that a gesture matches to this
+	 * gesture model.
+	 * 
+	 * @param gesture a gesture to test.
+	 * @return probability that the gesture belongs to this gesture
+	 * model.
+	 */
+	public double matches(Gesture gesture) {
+		int[] sequence = quantizer.getObservationSequence(gesture);
+		return this.markovmodell.getProbability(sequence);
+	}
+
+	/**
+	 * For debug purposes or very technical interested people. :)
+	 */
+	public void printMap() {
+		Log.write("Gesture Quantizer-Map:");
+		this.quantizer.printMap();
+	}
+	
+	/***
+	 * For debug purposes or very technical interested people. :)
+	 * @return
+	 */
+	public void print() {
+            Log.write(Log.DEBUG, "HMM-Print:", this);
+            this.markovmodell.print();
+            Log.write(Log.DEBUG, "Quantizer-Print:", this);
+            this.quantizer.printMap();
+	}
+	
+	public int getNumStates() {
+		return this.numStates;
+	}
+	
+	public int getNumObservations() {
+		return this.numObservations;
+	}
+	
+	/** 
+	 * Returns the model probability for bayes.
+	 * 
+	 * @return
+	 * 		the model probability
+	 */
+	public double getDefaultProbability() {
+		return this.defaultprobability;
+	}
+	
+	/**
+	 * Since the bayes classifier needs a model probability for
+	 * each model this has to be set once after training. As model
+	 * probability the average probability value has been choosen.
+	 * 
+	 * TODO: try lowest or highest model probability as alternative
+	 * 
+	 * @param defsequence the vector of training sequences.
+	 */
+	private void setDefaultProbability(Vector<Gesture> defsequence) {
+		double prob=0;
+		for(int i=0; i<defsequence.size(); i++) {
+			prob+=this.matches(defsequence.elementAt(i));
+		}
+		
+		this.defaultprobability=(prob)/defsequence.size();
+	}
+	
+	public void setDefaultProbability(double prob) {
+		this.defaultprobability = prob;
+		Log.write("def-prob. set to = "+this.defaultprobability);
+	}
+
+	public Quantizer getQuantizer() {
+		return this.quantizer;
+	}
+	
+	public void setQuantizer(Quantizer q) {
+		this.quantizer = q;
+	}
+	
+	public HMM getHMM() {
+		return this.markovmodell;
+	}
+	
+	public void setHMM(HMM hmm) {
+		this.markovmodell = hmm;	
+	}
+	
+}