Title: Addressing the Problems of Data-Centric Physiology-Affect Relations Modeling
Author: Roberto Legaspi, Ken-ichi Fukui, Koichi Moriyama, Satoshi Kurihara, Masayuki Numao, Merlin Suarez
Publisher: IUI '10, February 7-10, 2010 Hong Kong
Summary
The researchers of this paper used a data centric approach to come up with a way to use machine learning to define "affective states". They used an EEG helmet, the researchers attempted to classify and define emotions of the wearer. A problem they ran into was the enormous size of the data set.
Emotion sensing algorithms are generally O(n^2) or O(n^3) complexity which obviously slows the entire process to a crawl. Another problem they encountered was sensing changes in emotion. Emotions can change rapidly, perhaps before analysis can be completed. Several changes to the algorithms were suggested which looked to improve the performance of the machine.
Discussion
This paper was very technical. The thoughts behind it were interesting but there are so many complex theories and equations behind all of it it's hard to understand. The thought of classifying and defining emotions seems very foreign.
Summary
Emotion sensing algorithms are generally O(n^2) or O(n^3) complexity which obviously slows the entire process to a crawl. Another problem they encountered was sensing changes in emotion. Emotions can change rapidly, perhaps before analysis can be completed. Several changes to the algorithms were suggested which looked to improve the performance of the machine.
Discussion
This paper was very technical. The thoughts behind it were interesting but there are so many complex theories and equations behind all of it it's hard to understand. The thought of classifying and defining emotions seems very foreign.
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