Final Treatise

After two rounds of revision, I finally handed in the treatise, which is the biggest assessment for this project, on Wednesday.

The main body consists of 6 chapters -- the Introduction, Literature Review, Design, Analysis, Discussion and Conclusion. It also uses some graphs and tables to help illustrate the problem. The treatise shows what I have done and what I have learnt through the whole project from March to October.

Now, I will start to prepare the presentation on November 12th or 13th.

Draft Treatise

This week, I have been working on the draft version of the treatise, and submitted the soft copy online this afternoon.

Although I got several separated documents which I edited during the process, it still took me a great efforts to write the treatise. Despite of the compulsory description of experiment design, implementation and analysis, I would like to focus on the learning process and trade-off decisions I made during the whole project. Thanks to all the documentations, notes and online blogs, it was not too hard to recall all details in each phase.

Up to now, the draft treatise includes the introduction, literature review, experiment design, implementation work and part of the analysis. Next week, I will continue to work on the draft and want to finish the main body before Thursday evening.

Data Analysis 2.2

This week I finished the further analysis of symmetry of m by adjusting the height of the line, to move it up and down a bit. I also test the feature on external dataset as well. The result is similar to what I got from the middle line.

Then I organised the results and start to edit the draft treatise. I discussed with the supervisor about the outline, and got a clear idea of what to write in each part.

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.