title: Building HMM for Kinect based gesture recognition
body: <p>I have a conceptional problem. I'm creating a program that uses Kinect for gesture recognition. I have some gesture data divided on categories (circles, swipes, etc.). For now I analyze only one hand. I record all the frames (30fps).</p>
  
  <p>(*) For making my data discrete and position independent, I calculate angles between consecutive points.</p>
  
  <p>Now I want to create <code>hidden Markov models</code> for each gesture type.</p>
  
  <p>Now I need to determine a number of states for my <code>HMM</code>. How to do that? I thought about finding the longest gesture (in time). E.g. I have 3 gestures, first <code>1,2s</code>, second <code>1,4s</code> and third <code>1,5s</code>. So <code>1,5s</code> is the longest one. Now I want to apply (*) for each frame every 250 miliseconds (4 samples within a second). Because my longest gesture is <code>1,5s</code> long, so <code>NumberOfStatesForHMM = 1500ms / 250ms = 6 states</code> - and this should be pretty optimal?</p>
  
  <p>I'm not sure how should I define states for <code>HMM</code>:/ If my idea above is correct, how to count transition probabilities when there are (e.g.) 6 states and one gesture ends after <code>1s</code>, so I analyze 4 states (probabilities of transitions from states 4 to 5 and 5 to 6 are equal to 0?).</p>
  
  <p>I read <a href="http://www.waset.org/journals/ijeee/v3/v3-3-26.pdf" rel="nofollow">THIS</a> paper, but I'm not quite sure how to solve my problem...</p>