Data analysis 2.1

This week, I finished the first letter specific feature's analysis -- symmetry for letter m.

I took these steps to extract the feature

Step 1: scan the file, and if the gesture is m, find the bounding box and compute the middle line function y = b

Step 2: pass the b value (from step 1) and the corresponding list of points to find all crossing points, and store them in another list - cross point list. If two adjacent points Point 1 and Point 2 are on different sides of the line y = b, I consider the second point as a crossing point.

Step 3: filter some of the gestures according to the number of crossing points.

            Only 1 crossing point: maybe a gesture that was not logged properly

Too many crossing points (e.g. 12 crossing points in Gesture 31 from Subject 10): the subject wrote two m…

Step 4: compute length of segments, and check which is on the left and which is on the right.

Step 5: get the ratio between two segments. Here, I use right_segment_length / left_segment_length, and compute average value for each level

The result shows that there are only significant differences in limited conditions. I think this is a potential feature, but still need adjustment to investigate in more details.

Data Analysis 1.3

I finished statistical tests for basic features like dimensions of bounding box, pen pressure and length of the gesture. I mainly use statistical methods including t-test and ANOVA. The results are not as good as expected, but I still found something. For example, the gesture length in the session 1. However, how to combine my result with external dataset and gave a generalised explanation is not easy to do.

In the next phase, I will focus on the letter-specific geometrical features for letter a, g and m.

Data Analysis 1.2

Today, I added the what subjects wrote during the experiment to the logging files. As it was almost impossible to collect what they wrote on-fly without recognition package, I took letters down by observing them writing. However, the problem is that I have to add the letter tag manually in the logging to facilitate further analysis.

It took me longer time than I expected, and I also encountered several formatting problems. Finally, all the gesture has the corresponding letter tag at the end of the gesture. It helps me to understand the importance of constructing logging files. If the format is not right, it may bring a lot of problems in the future stages.

Data Analysis 1

I have finished the user experiment on Tuesday, and started the first phase of data analysis.

First of all, I computed the accuracy of all questions to get an overview of the task performance. Then, I did the Friedman ANOVA on the subjective feedback to validate my experiment design (i.e. difficulty for each level). 

In the next step, I'll continue to analyse the features I've tested on the external dataset, and focus on the letter specific features.