I've gone through Hidden Markov models (HMM) for the past few months. However there are a few things that are confusing.
The set up is simple: I have to model some human gestures such as walking, jumping, and falling. The observed data have been obtained via an accelerometer while the person was doing the movements.
I trained theses observations using the famous Baum-Welch algorithm to get the parameters of an HMM for some states. Further, using the Forward and Backward procedures, the likelihood of the observation sequences given the model (i.e., the parameters) were found.
Using a model selection criteria such as Akaike information criterion (AIC), I got the optimum states that represented the data:
a)Walking: 2 states b)jumping: 2 states c)Falling: 4 states
All these HMMs are then stored in a directory.
Lastly, Viterbi decoding is used to get the most likely sequence of hidden states that produced the data.
My questions are:
Suppose I performed the experiment again and I just get the data without knowing what kind of movement has been done. After getting the data trained, I got 2 states. How will the machine differentiate which kind of movement has been done, especially if walking and jumping are represented by 2 states?
Suppose the person has performed a different kind of gesture, e.g., sliding, what is the expected output after training? Will the machine be able to detect that or generate a false negative result?