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.

Preview of the Dataset - 2

Today, I finished the plot of the gesture, from which I got a clearer overview of the dataset. The python scripts were used to plot all the gestures according to the gesture position (x, y) and cognitive levels. The original size of the letter (about 4000 * 4000) is too big to display on the screen, so I zoomed them out to fit all the gestures from one subject into one screen (2056 * 1024). The reason why I arranged them in one screen is to make it easier to find potential features by comparing all the gestures at the same time. Currently, the gestures are ordered by time.

The next step for me is to look into those gestures and try to find interesting and valuable features to test. And another thing to be finished before the end of the next week is to summarise the frequently used features for further reference.