  Equipping social and service robots with the ability to perceive human
emotional intensities during an interaction is in increasing demand. Most of
existing work focuses on determining which emotion(s) participants are
expressing from facial expressions but largely overlooks the emotional
intensities spontaneously revealed by other social cues, especially body
languages. In this paper, we present a real-time method for robots to capture
fluctuations of participants' emotional intensities from their body poses.
Unlike conventional joint-position-based approaches, our method adopts local
joint transformations as pose descriptors which are invariant to subject body
differences as well as the pose sensor positions. In addition, we use a Long
Short-Term Memory Recurrent Neural Network (LSTM-RNN) architecture to take the
specific emotion context into account when estimating emotional intensities
from body poses. The dataset evaluation suggests that the proposed method is
effective and performs better than baseline method on the test dataset. Also, a
series of succeeding field tests on a physical robot demonstrates that the
proposed method effectively estimates subjects emotional intensities in
real-time. Furthermore, the robot equipped with our method is perceived to be
more emotion-sensitive and more emotionally intelligent.
