Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System Export

@incollection{haag, abstract = {The detection of emotion is becoming an increasingly important field for human-computer interaction as the advantages emotion recognition offer become more apparent and realisable. Emotion recognition can be achieved by a number of methods, one of which is through the use of bio-sensors. Bio-sensors possess a number of advantages against other emotion recognition methods as they can be made both inobtrusive and robust against a number of environmental conditions which other forms of emotion recognition have difficulty to overcome. In this paper, we describe a procedure to train computers to recognise emotions using multiple signals from many different bio-sensors. In particular, we describe the procedure we adopted to elicit emotions and to train our system to recognise them. We also present a set of preliminary results which indicate that our neural net classifier is able to obtain accuracy rates of 96.6\% and 89.9\% for recognition of emotion arousal and valence respectively.}, author = {Haag, Andreas and Goronzy, Silke and Schaich, Peter and Williams, Jason}, booktitle = {Affective Dialogue Systems}, citeulike-article-id = {3458848}, keywords = {bci, eeg, file-import-08-10-23, meditation}, pages = {36--48}, posted-at = {2008-10-29 00:50:37}, priority = {2}, title = {Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System}, year = {2004} }

TY - CHAP ID - haag L3 - citeulike-article-id:3458848 N2 - The detection of emotion is becoming an increasingly important field for human-computer interaction as the advantages emotion recognition offer become more apparent and realisable. Emotion recognition can be achieved by a number of methods, one of which is through the use of bio-sensors. Bio-sensors possess a number of advantages against other emotion recognition methods as they can be made both inobtrusive and robust against a number of environmental conditions which other forms of emotion recognition have difficulty to overcome. In this paper, we describe a procedure to train computers to recognise emotions using multiple signals from many different bio-sensors. In particular, we describe the procedure we adopted to elicit emotions and to train our system to recognise them. We also present a set of preliminary results which indicate that our neural net classifier is able to obtain accuracy rates of 96.6% and 89.9% for recognition of emotion arousal and valence respectively. EP - 48 TI - Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System T2 - Affective Dialogue Systems SP - 36 KW - bci KW - eeg KW - file-import-08-10-23 KW - meditation AU - Haag, Andreas AU - Goronzy, Silke AU - Schaich, Peter AU - Williams, Jason PY - 2004/// ER -