Publications

Method for Text Entry in Smartwatches Using Continuous Gesture Recognition

This work proposes a method that allows the entry of text in smartwatches using gestures based on geometric forms. For this it is proposed the development of a prototype capable of inserting a letter with no more than two user interactions. Gesture recognition is performed using the incremental recognition algorithm. A set of gestures with lines and curves were created to be recognized by the incremental recognition algorithm, generated from the reduced equation of the line and the reduced equation of the circumference, respectively. After recognizing the gestures, they are sent to a classifier Naïve Bayes which is responsible for predicting the letter that will be inserted. The Naïve Bayes classifier was trained with a user gesture base that drew all the letters of the alphabet using only the gestures available in the set presented to them. Using the gesture base and the classifier Naïve Bayes a prototype was developed for smartwatches that automatically suggests the most likely letters to be inserted. The prototype was used to perform an experiment, during the experiment the users inserted the five most frequent letters and the five less frequent letters of the English language. The results of the experiment show that the prototype is able to recognize a letter with at most two interactions between the user and the smartwatch. The analysis of the usability and experience test shows that the prototype has generalized potential for use, since it allows the entry of text with up to two interactions and with a 100% hit rate for the most frequent letters and 95.14% for less frequent letters.
Method for Text Entry in Smartwatches Using Continuous Gesture Recognition

Recursive diameter prediction for calculating merchantable volume of eucalyptus clones using Multilayer Perceptron

A very common problem in forestry is the realization of the forest inventory. The forest inventory is very important because it allows the trading of mediumand long-term timber to be extracted. On completion , the inventory is necessary to measure different diameters and total height to calculate their volumes. However, due to the high number of trees and their heights, these measurements are an extremely time consuming and expensive. In this work, a new approach to predict recursively diameters of eucalyptus trees by means of Multilayer Perceptron artificial neural networks is presented. By taking only three diameter measures at the base of the tree, diameters are predicted recursively until they reach the value of 4 cm, with no previous knowledge of total tree height. The training was conducted with only 10% of the total trees planted site, and the remaining 90% of total trees were used for testing. The Smalian method was used with the predicted diameters to calculate merchantable tree volumes. To check the performance of the model, all experiments were compared with the least square polynomial approximator and the diameters and volumes estimates with both methods were compared with the actual values measured. The performance of the proposed model was satisfactory when predicted diameters and volumes are compared to actual ones.
Recursive diameter prediction for calculating merchantable volume of eucalyptus clones using Multilayer Perceptron