Hidden Markov Models for Hand Gestures
You say that your goal is to create a model and compute the likelihood of a test sample using that model.  For that purposes, you do not need to find a way to interpret the hidden states, and you do not need to engineer the model so the hidden states have a meaning that you can understand.  Instead, let the standard Baum-Welch algorithm compute a reasonable model that fits the training data, and don't worry about the meaning of the hidden states.
If you had a physics model where you knew what the hidden states &quot;should&quot; be, based on some domain knowledge (e.g., about the movement patterns of bones and joints), then you could incorporate that into your model in hopes of improving the model... but that's not required.  Since you don't seem to start from such a place, there is no need to try to force the hidden states to take on any particular human-understandable meaning.