Emotion sensing from head motion capture

Computational analysis of emotion from verbal and non-verbal behavioral cues is critical for human-centric intelligent systems. Among the non-verbal cues, head motion has received relatively less attention, although its importance has been noted in several research. We propose a new approach for emotion recognition using head motion captured using Motion Capture (MoCap). Our approach is motivated by the well known kinesics-phonetic analogy, which advocates that, analogous to human speech being composed of phonemes, head motion is composed of kinemes i.e., elementary motion units. We discover a set of kinemes from head motion in an unsupervised manner by projecting them onto a learned basis domain and subsequently clustering them. This transforms any head motion to a sequence of kinemes. Next, we learn the temporal latent structures within the kineme sequence pertaining to each emotion. For this purpose, we explore two separate approaches – one using Hidden Markov Model and another using artificial neural network. This class-specific, kineme-based representation of head motion is used to perform emotion recognition on the popular IEMOCAP database. We achieve high recognition accuracy (61.8% for three class) for various emotion recognition tasks using head motion alone. This work adds to our understanding of head motion dynamics, and has applications in emotion analysis and head motion animation and synthesis

On the role of head motion in affective expression

Non-verbal behavioral cues, such as head movement, play a significant role in human communication and affective expression. Although facial expression and gestures have been extensively studied in the context of emotion understanding, the head motion (which accompany both) is relatively less understood. This paper studies the significance of head movement in adult's affect communication using videos from movies. These videos are taken from the Acted Facial Expression in the Wild (AFEW) database and are labeled with seven basic emotion categories: anger, disgust, fear, joy, neutral, sadness, and surprise. Considering human head as a rigid body, we estimate the head pose at each video frame in terms of the three Euler angles, and obtain a time-series representation of head motion. First, we investigate the importance of the energy of angular head motion dynamics (displacement, velocity and acceleration) in discriminating among emotions. Next, we analyze the temporal variation of head motion by fitting an autoregressive model to the head motion time series. We observe that head motion carries sufficient information to distinguish any emotion from the rest with high accuracy and this information is complementary to that of facial expression as it helps improve emotion recognition accuracy

Bridging the gap between emotion and joint action

Our daily human life is filled with a myriad of joint action moments, be it children playing, adults working together (i.e., team sports), or strangers navigating through a crowd. Joint action brings individuals (and embodiment of their emotions) together, in space and in time. Yet little is known about how individual emotions propagate through embodied presence in a group, and how joint action changes individual emotion. In fact, the multi-agent component is largely missing from neuroscience-based approaches to emotion, and reversely joint action research has not found a way yet to include emotion as one of the key parameters to model socio-motor interaction. In this review, we first identify the gap and then stockpile evidence showing strong entanglement between emotion and acting together from various branches of sciences. We propose an integrative approach to bridge the gap, highlight five research avenues to do so in behavioral neuroscience and digital sciences, and address some of the key challenges in the area faced by modern societies

2nd Edition of Health Emergency and Disaster Risk Management (Health-EDRM)

Disasters such as earthquakes, cyclones, floods, heat waves, nuclear accidents, and large-scale pollution incidents take lives and incur major health problems. The majority of large-scale disasters affect the most vulnerable populations, which often comprise extreme ages, remote living areas, and endemic poverty, as well as people with low literacy. Health emergency and disaster risk management (Health-EDRM) refers to the systematic analysis and management of health risks surrounding emergencies and disasters, and plays an important role in reducing the hazards and vulnerability along with extending preparedness, responses, and recovery measures. This concept encompasses risk analyses and interventions, such as accessible early warning systems, the timely deployment of relief workers, and the provision of suitable drugs and medical equipment to decrease the impact of disasters on people before, during, and after an event (or events). Currently, there is a major gap in the scientific literature regarding Health-EDRM to facilitate major global policies and initiatives for disaster risk reduction worldwide