Data Analysis - 1.2

I finished the width/height ratio normalisation, and then I did the t-test to compare the new normal and stress results. There are no significant differences between them, with p-value (two-tail) > 0.05. Although this feature fails to be a good feature, I learnt a lot from the normalisation process. What is more, I think this idea can be used in the future analysis as well, because the differences between letters may also have other impacts on the gestures.

Data Analysis-1.1

After discussing with my supervisor, he suggest me to normalise the width/height ratio for different letters, so that it can eliminate the effect of the generic shape of the letter. Then, I will explore more about the bounding box, to understand it in more details and compare my results with the experimenter's results.

In the further analysis, I need to use the actual written letter to group gestures, instead of the automatic recognition, so I need another field in the Stroke called "written". Since I have already summarised all the recognition errors, I will use the list to correct recognition result for each gesture. 

Data Analysis - 1

I would like to start the analysis from an easy and straightforward feature -- bounding box. First of all, this week I wrote some scripts to abstract bounding box information from processed data (one file for each subject), like the width, height, and width over height ratios. Then, I computed average values for different sessions (Normal and High). I will use t-test to compare two groups of results.

However, there are some issues I want to discuss with the supervisor. The most important one is the letter's shape has significant effect on the bounding box, and I am not sure whether I can compare the result. Additionally, I am not sure how to deal with slant, which the stoke is not vertical to the plane, because slant stroke will change the dimension of bounding box.

Progress Report - 2

I have finished the progress report, and it helped me to summarise what I have done in this semester, especially the papers I have read. 

At the end of the report, I attached a new Gantt Chart, adjusting the time allocation of the rest tasks. There are really a lot of things to do in the next stage, as my data analysis and experiment implementation are a bit behind the schedule.  As to the literature review, I still need more articles about the pen-input features.

Progress Report

Today, I started editing the progress report. It will cover the project introduction, literature review, current progress, future work and revised project plan.

For the most important part -- literature review, I would like to summarise the notes I took in the previous two months, and introduce the fundamental theory under my project, including working memory model, cognitive load theory, measurement and research on the pen gesture. The current progress will cover the data analysis of the external dataset, and my experiment design version 1.0.

My aim is to finish this report before the end of next week, and discuss with the supervisor for any issues in it.

Some ideas about the experiment design

Today, I discussed experiment design with my supervisor. Currently, I designed three different tasks, which are graph transforming task, counting task and math operation task.The tasks involve recalling, applying rules and processing information. They use number of rules, time limit and amount of information to manipulate the cognitive load levels.

As my hypothesis for this experiment is related to the cognitive recovery time, so an important point in the experiment is how to control the task order and rest time between them. Now, I haven't come up with the detailed plan, but I would do more research on this topic and conduct some pilot study on it in June or July.

The supervisor also reminded me that I need to think about how to make the task adaptive to different people according to their real-time performance. 

Features for data analysis

About plotting, I added labels, including the gesture number, cognitive load level and recognise result, on each gesture to show the attribute clearer. 

Also, I reviewed two papers discussing the gesture features. They are all about the geometrical features of a gesture, ranging from simple ones like length, bounding box, and some complicated ones curvature and sharpness. In the papers, these features were used in handwritten recognition or input mode detection. They gave me some ideas about the feature selection, and I will search for more papers in the topic "online input recognition" (--> general features) and "handwriting verification" (--> subjective features). For my study, the features should be sensitive to the cognitive load and easy to test. I think the sharpness and other features related to angles between certain points might be potential good features, and I will summarise and test them in the next stage.