Simple gesture solution

I recently read “Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes”, a UIST ’07 paper by Jacob O. Wobbrock, Andrew D. Wilson, and Yang Li. The paper describes a problem facing many interface designers today, which is the complexity and exclusiveness of gesture recognition software. They designed a cheap, easy solution, which can be implemented anywhere by anyone with around 100 lines of code. The following is a list of the requirements that they set for the new software:
1. be resilient to variations in sampling due to movement speed or sensing;
2. support optional and configurable rotation, scale, and position invariance;
3. require no advanced mathematical techniques (e.g., matrix inversions, derivatives, integrals);
4. be easily written in few lines of code;
5. be fast enough for interactive purposes (no lag);
6. allow developers and application end-users to “teach” it new gestures with only one example;
7. return an N-best list with sensible [0..1] scores that are independent of the number of input points;
8. provide recognition rates that are competitive with more complex algorithms previously used in HCI to recognize the types of gestures shown in Figure 1.

The following paragraph is a simple description of how the software operates. First the raw data points are collected from the hardware. The raw data is resampled and the excess points are eliminated. It was found that a set of 64 equidistance points was adequate without being excessive. Imagine that a line is drawn from the start point to the center of the completed gesture. The object is then rotated to align this line to zero degrees. The gesture is then scaled to a reference square. Optimal alignment is then calculated. Finally the gesture is compared to the gesture templates. Testing was performed with the sixteen basic gestures pictured above. Despite being far less complex, $1 was found to have nearly identical results as DTW and superior results compared to Rubine.

Images from “Gestures without Libraries, Toolkits or Training: A $1 Recognizer for User Interface Prototypes” (Image1 from page 1) (Image2 from page4)