  There is an increasing consensus among re- searchers that making a computer
emotionally intelligent with the ability to decode human affective states would
allow a more meaningful and natural way of human-computer interactions (HCIs).
One unobtrusive and non-invasive way of recognizing human affective states
entails the exploration of how physiological signals vary under different
emotional experiences. In particular, this paper explores the correlation
between autonomically-mediated changes in multimodal body signals and discrete
emotional states. In order to fully exploit the information in each modality,
we have provided an innovative classification approach for three specific
physiological signals including Electromyogram (EMG), Blood Volume Pressure
(BVP) and Galvanic Skin Response (GSR). These signals are analyzed as inputs to
an emotion recognition paradigm based on fusion of a series of weak learners.
Our proposed classification approach showed 88.1% recognition accuracy, which
outperformed the conventional Support Vector Machine (SVM) classifier with 17%
accuracy improvement. Furthermore, in order to avoid information redundancy and
the resultant over-fitting, a feature reduction method is proposed based on a
correlation analysis to optimize the number of features required for training
and validating each weak learner. Results showed that despite the feature space
dimensionality reduction from 27 to 18 features, our methodology preserved the
recognition accuracy of about 85.0%. This reduction in complexity will get us
one step closer towards embedding this human emotion encoder in the wireless
and wearable HCI platforms.
