Reading Assignment: Protractor: A Fast and Accurate Gesture Recognizer

Reference Information
Title: Protractor: A Fast and Accurate Gesture Recognizer
Author: Yang Li
Citation: "Protractor: A Fast and Accurate Gesture Recognizer", Yang Li, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2169-2172, 2010.

Summary
This paper discussed Protractor, which is a template-based gesture recognizer that uses nearest neighbor for classifying new gestures based on a set of pre-classified training gestures. The nearest neighbor approach works by comparing an unknown gesture at runtime to the training examples, and classifying the unknown one as that of the class of its K nearest neighbors in terms of similarity. The method used for comparing the similarity of gestures with Protractor is claimed to be novel, using minimum angular distance to calculate similarity. This contributes to the recognizer's accuracy, speed, and minimization of memory usage. It was suggested that these features of Protractor make it a good choice for use on mobile devices, where memory and processing power are in limited supply. The usage of nearest neighbors also allows for personalization by users. Preprocessing of gestures was performed before comparison, involving resampling and noise reduction similar to that of the $1 recognizer. One of the more unique features of Protractor is its ability to recognize variation with regards to the orientation of a gesture.

Protractor was compared to the $1 recognizer during evaluation, and a similar experiment was conducted using the same data that the $1 recognizer was tested on in its comparisons to other recognizers. The results of the experiment showed that Protractor performs faster, no less accurately, and uses less memory than the $1 recognizer. In addition, the experiment was performed on a mobile device to analyze its usefulness for mobile applications.

Thoughts
The main concern that I had while reading about Protractor is the slowness that generally comes with the usage of nearest neighbors algorithms. Since the algorithm requires comparisons at runtime to each of the known gestures, the runtime computations could be costly.Therefore, I was rather surprised that the experiments run on a mobile device showed it to be a feasible solution for mobile applications. I thought that this was a great test to perform, as memory and time constraints are something that must be considered for mobile platforms.

In addition, the ability to use a nearest neighbors recognizer that is both fast and that requires smaller amounts of memory is beneficial due to the amount of personalization that can be provided by its use. Since the nearest neighbors algorithm simply needs to compare gestures based on their associated data, it is much simpler for gestures to be added to the database, allowing for more opportunities for user-customization. This attribute of Protractor could allow it to be applied to more applications. Combined with its ability to run reasonably on mobile devices, this opens up even more possibilities for usage of the Protractor recognizer.