Algorithm for gesture classification in a wearable
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.