|Budget Amount *help
¥16,130,000 (Direct Cost : ¥15,500,000、Indirect Cost : ¥630,000)
Fiscal Year 2007 : ¥2,730,000 (Direct Cost : ¥2,100,000、Indirect Cost : ¥630,000)
Fiscal Year 2006 : ¥5,200,000 (Direct Cost : ¥5,200,000)
Fiscal Year 2005 : ¥8,200,000 (Direct Cost : ¥8,200,000)
We conducted this research with three steps in 2005-2007 fiscal years.
In the first step, “extracting possible movie features that may influence physiological responses, “we firstly cut 60 scenes of 20 seconds from several varieties of movies and extracted 78 physical movie features. Secondly, we constructed a 2-D psychological space, called an emotional space, consisting of(tension-relax)axis and(refreshing-gloomy)axis, and narrowed 30 from 78 possible movie features not using a physiological experiment but a psychological one.
In the second step, “extracting relationship between movie features and physiological responses, “we firstly made abstract experimental movie scenes with 20 different variables of one feature that shown the highest correlation among 30 features. Since movie context, i.e. story, deeply influences to human emotion, we made the abstract movie scenes to avoid its effect. Secondly, we measured physiological data by displaying the abstract movies. Measured data are EEG at two side points and two back side, ECG, sweating rate, blood pressure level, and finger pulse volume. However, the difference of measured data among subjects was bigger than the difference of values of physical movie features, we have not found out the significant relationship between the movie features and physiological responses yet.
In the third step," establishing driving emotion using extended interactive evolutionary computation(IEC),"we aimed to establish a technique to optimize movie features to drive a movie viewer's physiological condition to a given target condition. Since we have not found out the movie features that surely drive human physiology in the second step, we focused on improving IEC technique. What we did was combining IEC with evolutionary multi-objective optimization, introducing Particle Swarm Optimization instead of evolutionary computation, introducing IEC user's evaluation model, and others.