1. A method of operating a gesture identification system to identify a user-performed gesture, the gesture identification system including at least one sensor responsive to user-performed gestures and a processor communicatively coupled to the at least one sensor, the method comprising: providing at least one signal from the at least one sensor to the processor; segmenting the at least one signal into data windows; for each i th data window in at least a subset of the data windows: determining a window class for the i th data window by the processor, the window class selected by the processor from a library of window classes, wherein each window class in the library of window classes exclusively characterizes at least one data window property; determining, by the processor, a respective probability that each gesture in a gesture library is the user-performed gesture based on a) the window class for the i th data window and, when i>1, b) the window class for at least one j th data window, where j<i; and identifying a highest-probability gesture for the i th data window by the processor, the highest-probability gesture corresponding to the gesture in the gesture library that has a highest probability of being the user-performed gesture for the i th data window; and identifying the user-performed gesture by the processor based on the respective highest-probability gestures for at least two data windows in the at least a subset of data windows.
9. The method of claim 1 wherein the at least one sensor includes at least one muscle activity sensor selected from the group consisting of: an electromyography (EMG) sensor and a mechanomyography (MMG) sensor, and wherein: providing at least one signal from the at least one sensor to the processor includes providing at least one signal from the at least one muscle activity sensor to the processor.