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