I am completing a final year project for hand gesture recognition using Hidden Markov Models
I am looking to implement multiple Hidden markov models where each model corresponds to a single gesture, similarly to this paper where the observed states are the angles between the coordinates of different points. This would create a sequence of numbers from 0 to 18 as seen in Figure 3 and Figure 4. .
What would the hidden states be in terms of this scenario?
The weather example has the observations 'Walk', 'Shop' and 'Clean' which would be the numbers 0-18 in the hand gesture case, however I do not know what the states 'Rainy' and 'Sunny' would correspond to in the hand gesture scenario.
Edit: I am generating a sequence of numbers that will correspond to a certain gesture using the method mentioned above. I will then use that sequence to train a HMM and will then test that HMM using another set of recorded numbers similar to the training set. Here is an example of my scenario:
Recorded data of observed states (theta) during a gesture:
observations = [0,1,4,15,4,3,1,0,19,18,17,16,15,15,16,3,1,1,0,18...]
Recorded data of test gesture:
test = [0,2,4,15,4,2,1,0]
My goal is to create a model from the first set of observations (which will be much longer as the gesture will be recorded many times) and determine the likelyhood of the test gesture to be from said model.
Will I need to generate a hidden state to create an accurate model of the gesture or can I just use unsupervised training for a model?
If i do have to use supervised training, should I create the hidden states using quadrants (i.e. 0-90 degrees = quadrant1, 90-180 degrees = quadrant2...)?