GSoC Accelerometer-based Gestures Update 4

[Koen Kooi]

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Op 23 jun 2008, om 08:35 heeft Paul-Valentin Borza het volgende  
geschreven:

> I've been busy the previous week as I had to finish writing my  
> bachelor's thesis on the gestures.
> Daniel Willmann - my mentor - sent the Neo FreeRunner to me on  
> Friday. It still hasn't arrived, but it's now in Hungary and will  
> arrive this week. That's definitely good news :)
> Like I've said to Mickey and Daniel while we were in the proposal  
> phase of GSoC, I have to implement some tweaks in HMMs to achieve  
> better results.
> State duration (a.k.a. duration modeling) will be used to prune  
> unpromising models in the recognizer.
>
> Like I've said, each model (gesture) has a number of states; each  
> can have its own number of states and these states differ from one  
> model to the other.
> Since I'm using a left-to-right model topology, the decoded sequence  
> must be something like this: 1 2 3
> where 1 is the initial state, and 3 is the final state.
> If the last state is not the final state (the model doesn't reach  
> the final state) then it's clearly not a match (trivial).
>
> Also here's what happens with models that aren't a match. Note that  
> the length of the state sequence is the length of the observation  
> sequence (HMM assumption).
> 12333333333333333333333333333333333333333333344
> It's clear that the state duration should be proportional with the  
> length and a model that generates a state sequence like that should  
> be pruned.
> Instead, I'm looking for something like
> 1111111122222222223333333333444444444
>
> This is just a simple optimization that can improve the accuracy.  
> After I prune those models, I look at the posterior probabilities  
> and decide on the best match.

Saurabh Gupta also has an fixed-point HMM implementation IIRC.

regards,

Koen
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