Algorithm for gesture classification in a wearable I'm fairly new to machine learning so please pardon me if I mess up terms. I'm working on a wearables project that involves hand gesture detection, looking for any pointers on potential algorithms that would be applicable. 

Basically there will be sensors that pick up tendon movement at the wrist to provide input, so for simplicity I'm assuming that would mean 5 features (one for each finger), each with values ranging from say 0-100. 

There are also 5 classes that the gesture could be classified as. The user would do each type of gesture several times to provide training data, and then for each gesture class I'd need an algorithm to determine the best model for it based on that training data. 

After initial training, whenever the user does a gesture it would input the sensor readings into each model to see which one is the closest match.
Additionally, since this is a wearable anything that optimizes for low memory and low processing power would be best.

If anyone has any input on either the type of models or the algorithm with which to generate them that I should look into I'd appreciate it!

Edit: Here is some sample data from 4 sensors during 3 different gestures. These are basically just pressure readings from sensors lying along tendons on the wrist.
It might be really useful to provide a bit more details regarding the actual data, produced by your sensor. Can you provide a simple example for any simple gesture? 

Anyway, let's try to address the problem in the following way: 


sensor can be represented as a function f(t)
range of the function will be {1,2,3,..100} 


So we can draw something like the following (can we?): 



You can extract various high-level features out of that data, but I would suggest to go with inflection points as the most straightforward option:



Those points, basically, define a pattern. I'll use a figure from the different answer to illustrate the idea. For example, the naive pattern below is used to identify a circle. 

 

In your case situation is a bit simpler, because sensor output is the only data you can work with. Naturally, you can combine multiple sensors in order to define a bit more complex interaction scenarios:



Also you can take into account f'(t) in order to distinguish between slow and fast movements, etc.  

Update

Thanks for the data. It does look like these three gestures looks pretty much the same from the data perspective (see visualization below). 



It really looks like several features are required to identify the gesture.