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.