Tuesday, March 30, 2010

Literature review plan and 'Writing a Thesis'

After meeting with Rafa and other project students yesterday, I had a look through the handout titled "Making a Strong Start" (from "How to Write a Better Thesis", Second Edition, David Evans, Paul Gruba. Melbourne University Press, 2002), and lecture slides on how to write academic introductions.

At the end of the chapter, the article summarizes the following about thesis structure:

The Standard Structure is composed of 4 parts:
  1. Introduction - Outline the problem and how you'll be tackling it, scope and how the thesis will be structured.
  2. Background chapters - Material required (essentially a literature review) prior to your work.
  3. An account of your own work - Begin with a formal statement of your hypotheses or research questions, propose a methodology to test your hypotheses (including reasoning for selection) and report the results.
  4. Synthesis - Discuss the implications of results, draw conclusions and modify existing theory (or create a new one).
I also had a look through another article ("Towards a Framework of Literature Review Process in Support of Information Systems Research", Yair Levy, Timothy J. Ellis, 2006 Informing Science and IT Education Joint Conference, 2006) for my literature review (and eventually for my actual Treatise).

Sunday, March 14, 2010

First meetings

Last week I met briefly with Rafa who he gave me an overview of the nature of work that the LATTE group did. After this, I also met with Omar Alzoubi (one of the LATTE group members) who showed me around the lab, the equipment they used to collect physiological signals (EEG, ECG, EMG, SC, etc.), and possible areas I could focus my project on.

Edit: Omar suggested I could run a full study (with 25-30 candidates) measuring physiological responses using their gear and then analysing the data, or I could work on improving some of the DSP algorithms they use to analyse data (e.g. ECG waves).

Currently, Rafa has suggested that I focus on Affective computing modelling. The problem involves taking the physiological response from subjects when exposed to stimuli and create a unique/personalized 'profile' for a subject's emotional response.

This could require the development of software that analyzes the results and qualitatively or quantitatively measures the response. E.g. For a particular subject, an increase in defensive blinking and an increase in rate of respiration could indicate a that this subject is experiencing strong emotions related to panic or sadness.

Difficulties in this project I can foresee:
  • Creating an individual profile of affect. Omar explained briefly that a certain emotion might be more prevalent given different pre-conditions (e.g. perhaps subjects won't have the same physiological response on different days), so repeated test data might be necessary.
  • How to relate or categorize emotions given different physiological signals and present them as qualitative/quantitative data. A framework must be established that would map certain responses to certain types of emotions (e.g. How do we measure 'happiness'? What sort of units would we use?).

Things to do for this week:
  • Read into articles about diagnostic assessment of emotional recognition (previously I had only read into predictive assessment).
  • Research previous suitable frameworks available that fits the processes LATTE uses.

Thursday, March 4, 2010

Affective Computing Thesis/Project

This online journal will be a record of my progress in my ELEC4712/4713 project. The topic is in the area of Affective Computing: "Electrophysiological signals for the recognition of emotion"

My primary supervisor is Rafael Calvo. Another point of reference from last year (just before the break) was Judy Kay, but at this point she hasn't been listed as a co-supervisor yet (some admin stuff I'll have to sort out soon!).

Actual research related material:

During the break I have been reading up on material given to me last year by Judy and Rafael about affective computing, user modelling and Dynamic Bayesian networks. Although I think my project will ultimately focus on using a diagnostic approach to model dynamic decision networks (DDNs), it is interesting to look at the alternative methods. Judy pointed me towards a paper that looks into modelling DDNs by predictive assessment ("Empirically building and evaluating a probabilistic model of user affect", Conti and Maclaren, 2009).

The article above also made reference into OCC theory, so I looked further into this in an article by Adam, Herzig and Longin ("A logical formalization of the OCC theory of emotions", 2009).