Recent developments in computer vision technologies within fields as diverse as surveillance, bio-informatics and the film industry, together with advanced image processing algorithms, present opportunities for the development of gestural computer interfaces allowing physical gesture (e.g. a ‘thumbs-up’ signal with the hand) to be interpreted by the computer.

This research is applying such technologies, together with the results of recent research on human biomechanics and neurobiology, in the development of an ergonomic computer interface that uses hand gestures distinguishable and interpretable by a computer vision system. Integral to this work is the development of a formal gestural syntax and an associated classification system.

I will explore this question by integrating observational drawing practice and theory with empirical eye-tracking tests.

I aim to find quantitative and qualitative differences between artists and non-artists in the way they look at objects, and suggest why these differences exist and how they come about.

My objectives are to:
1) Contribute new quantitative information about artist’s eye movements, and interpret their meaning for different ways of seeing.
2) Integrate findings from phenomenological investigation of drawing with measurements of gaze during drawing.
3) Enhance knowledge of the effects of drawing skill acquisition on perceptual habits.
4) Contribute to art education debate concerning drawing and skill acquisition.

Research Subject:
Artists seek to minimise possible bias caused by what they already know about an object. The approach is paradoxical: In order to render the object recognisable (in a 2-d image), artists attempt to forget what the know and to see the object anew.
Do artists ever achieve this?
If so, how do they do it?
Can eye-tracking shed light on the processes involved in this special case of perception?

Methodology:
My research will adopt an integrated approach to the study of perception, relating phenomenological analysis to the biological findings of neuroscience.
I will use established methods, as used by Dr Tchalenko and other psychophysical researchers, for eye tracking experiments. These will be carried out as subjects draw from life in tests that highlight different visual strategies for information capture. I will use methods developed by contemporary cognitive phenomenologists who argue for dual awareness of cognition, attending to both scientific and experimental issues. They suggest practical solutions to the problem of integrating first and third person methods and the importance of awareness of embodied experience, in line with the ideas of Merleau-Ponty. I will assess my acquisition of drawing skills and application of phenomenological methods using a detailed diary and in consultation with Michael Roth and Shaun Gallagher, experts in the theory and practice of cognitive phenomenology and skill acquisition.

Ethical considerations:
In year 2 of my project I will conduct experiments with a dozen students at Camberwell College of Arts as subjects.
The experiments will pose no threat to participants. Eye tracking is non-invasive and there are no health risks involved.
I will gain their consent using an information sheet and signed consent form.
I will follow procedures (as outlined in my research ethics approval form) to safeguard the data and participants’ confidentiality.

Jem Mackay is Research Assistant to John Tchalenko, working on tracking artists' eye movements. His particular interest is how the brain co-ordinates the different activities involved in drawing. Jem is also a first year PhD student at the University of the Arts, London and this interest feeds back into his own research, which explores the most effective structures for online creative collaboration. His theory explores how neural networking can be simulated on the Internet between multiple users.

Generative music is commonly agreed to describe music in which a system of process is composed to generate music rather than the composition of the direct musical event which will result from that system. As generative music is created by the composition of music within a system or process this means that musicians, artists, producers, and to some extent the composer, besides defining the musical parameters within the piece, essentially separates himself from the creation of the final piece of music.

Studies have found that people’s music tastes are highly individual with different types of music affecting people differently. Music has also been found to counteract stress, anxiety and pain. Furthermore, music has been found to promote relaxation and comfort in patients in health settings. The type of music played can also influence physiological parameters such as body temperature, heart rate, blood pressure, respiration rate and production of cortisol and salivary Iga in patients.

Decreased stress and anxiety in patients could have positive outcomes for both patients and their carers. The feelings of stress and anxiety have a number of contributing factors including fear and pain or of the treatment outcome and also the patients’ feelings of loss of control over their body and immediate environment. Physical reactions to stress and anxiety include restlessness, trembling, muscle tension, fatigue, shortness of breath, increased heart rate and elevated blood pressure that could lead to medical complications.

Decreased levels in stress and anxiety could also result in an improvement in the feeling of well being in the patient, a decreased length of hospital stay, decreased medications such as pain relievers and sedatives and an overall increased perception of the patients’ stay in hospital.

The ability of generative music system to compose and alter the music it creates in real-time and the ability of the music creation process to be further influenced by changes in information received via an interactive tangible interface of graphical user interface means that there are a large number of potential areas in which generative music systems could be applied. This research proposes to develop a generative music system that comprises of three components: the first a generative music software component that is capable of composing music and altering the characteristics of the music it creates in real-time. The second component, a tangible interface with bio-inputs, will enable physiological data taken from a subject to influence the characteristics of the music created by the generative music software as will the third component to the generative music system, a graphical user interface. Furthermore, this research proposes to identify and develop applications for the generative music system that could be potentially employed in health settings to promote positive states of mental and physical well being and also perform a clinical study of effectiveness of those applications.